阅读说明：本技术 监测设备及系统 (Monitoring device and system ) 是由 马塞洛·马利尼·拉梅戈 伊莎多拉·布蒂克斯基·拉梅戈 拉丽莎·布蒂克斯基·拉梅戈 于 2019-08-26 设计创作，主要内容包括：公开了用于若干应用的临床级监测技术,在所述应用中,低成本、无线、多参数、单次使用和多次使用医疗设备和健身设备和/或健康设备是有用和有益的。一种被附接或放置在测量位置上的监测设备包括：光传感器、温度传感器或第一电接触传感器和第二电接触传感器中的至少一种。从光传感器、温度传感器和/或第一电接触传感器和第二电接触传感器接收的信号可以被传送到主机设备。一种主机设备上的应用程序可以处理所述信号,以计算一个或多个生理参数、波形数据、趋势数据和/或一个或多个报告。(Clinical-level monitoring techniques are disclosed for several applications in which low-cost, wireless, multi-parameter, single-use and multi-use medical and fitness and/or health devices are useful and beneficial. A monitoring device attached or placed on a measurement location comprising: a light sensor, a temperature sensor, or at least one of a first electrical contact sensor and a second electrical contact sensor. Signals received from the light sensor, the temperature sensor, and/or the first and second electrical contact sensors may be communicated to a host device. An application on a host device may process the signals to calculate one or more physiological parameters, waveform data, trend data, and/or one or more reports.)

1. A monitoring device, comprising:

a light sensor within a housing of the monitoring device and comprising a light source and a photodetector disposed adjacent a first surface of the housing, wherein the light source is operable to emit light toward a measurement location of a first body part of a user when the first surface is in contact with the measurement location and the photodetector is operable to receive light reflected from the measurement location;

a temperature sensor within the housing of the monitoring device and disposed adjacent the first surface of the housing and operable to measure a temperature at the measurement location when the first surface is in contact with the measurement location;

a first electrical contact sensor within the housing of the monitoring device and disposed adjacent the first surface of the housing to contact the measurement location when the first surface is in contact with the measurement location;

a second electrical contact sensor within the housing of the monitoring device and disposed adjacent to a second surface of the housing, wherein the first electrical contact sensor and the second electrical contact sensor detect cardiac signals when a different second body part of the user contacts the second electrical contact sensor; and

a wireless communication device operable to communicate the signals received from the photodetector, the temperature measurements, and the cardiac signal to an application on a host device.

2. The monitoring device of claim 1, further comprising: a processing device operable to connect to the light sensor, the temperature sensor, and the first and second electrical contact sensors and operable to process higher frequency, lower latency, or lower complexity calculations using signals received from the photodetector, temperature measurements, or the cardiac signal, and the application on the host device is operable to process lower frequency, higher latency, or higher complexity calculations.

3. The monitoring device of claim 1, wherein the measurement location is an ear of the user.

4. The monitoring device of claim 1, wherein the measurement location is a finger of the user.

5. The monitoring device of claim 1, wherein the measurement location is a forehead of the user.

6. The monitoring device of claim 1, wherein the monitoring device is incorporated into a hat worn by the user.

7. The monitoring device of claim 6, wherein:

the cap further comprises a plurality of electroencephalographic (EEG) electrodes; and is

The wireless communication device is operable to communicate signals from the plurality of EEG electrodes to the application on the host device.

8. The monitoring device of claim 1, wherein the monitoring device is incorporated into a patch that is attached to the measurement location.

9. A system, comprising:

a monitoring device, comprising:

a light sensor within a housing of the monitoring device and comprising a light source and a photodetector disposed adjacent a first surface of the housing, wherein, when the first surface is in contact with a measurement location of a first body part of a user, the light source is configured to emit light toward the measurement location and the photodetector is configured to receive light reflected from the measurement location;

a temperature sensor within the housing of the monitoring device and disposed adjacent to the first surface of the housing and configured to measure a temperature at the measurement location when the first surface is in contact with the measurement location;

a first electrical contact sensor within the housing of the monitoring device and disposed adjacent the first surface of the housing to contact the measurement location when the first surface is in contact with the measurement location;

a second electrical contact sensor within the housing of the monitoring device and disposed adjacent to a second surface of the housing, wherein the first electrical contact sensor and the second electrical contact sensor detect cardiac signals when a different second body part of the user contacts the second electrical contact sensor; and

a wireless communication device operable to transmit, the signals received from the photodetector, the temperature measurements, and the cardiac signal; and

an application on the host device operable to:

processing signals transmitted by the monitoring device to calculate a physiological parameter, waveform data associated with the physiological parameter, and trend data associated with the physiological parameter; and

causing the physiological parameter and at least one of a waveform associated with the physiological parameter or a trend associated with the physiological parameter to be displayed.

10. The system of claim 9, wherein:

when the physiological parameter exceeds an upper limit, the application generates a first alert; and

the application generates a second alert when the physiological parameter is less than a lower limit.

11. The system of claim 10, wherein the upper limit and the lower limit are set in a user settings interface of the application.

12. The system of claim 10, wherein the physiological parameter is one of pulse rate, perfusion index, or blood oxygen saturation.

13. The system of claim 10, wherein the first processing device is operable to process higher frequency, lower latency or lower complexity calculations using signals received from the photodetector, temperature measurements or the cardiac signal, and the application on the host device is operable to process lower frequency, higher latency or higher complexity calculations.

14. The system of claim 10, wherein the measurement location is an ear of the user.

15. The system of claim 10, wherein the measurement location is a finger of the user.

16. The system of claim 10, wherein the measurement location is the forehead of the user.

17. The system of claim 10, wherein the application causes a screen to be displayed to enable a user to share the physiological parameter, or trend data with another computing device.

18. The system of claim 10, wherein the application:

causing a screen to be displayed that enables a user to select a report to be shared with another computing device, the report including analysis data of waveform data or trend data associated with the physiological parameter; and

based on the selected report, generating the report and causing the report to be transmitted to the other computing device.

19. The system of claim 18, wherein the report comprises one of:

a temporal blood oxygen saturation measurement;

(ii) blood oxygen saturation distribution;

pulse rate measurements over time;

a pulse rate distribution;

pulse rate fluctuation distribution;

perfusion index measurements over time;

perfusion index distribution; or

Perfusion index log-fluctuating distribution.

20. The system of claim 10, wherein:

the monitoring device is incorporated into a hat worn by the user; or

The monitoring device is incorporated into a patch that is attached to the measurement site.

Background

Devices for determining various health parameters are often used by consumers and medical personnel. For example, measurements of blood oxygen saturation (SpO2), Pulse Rate (PR), and Perfusion Index (PI) are health parameters monitored by consumers and medical personnel for receiving feedback regarding the health and/or fitness of a user.

Disclosure of Invention

Embodiments disclosed herein enable clinical-level monitoring techniques in several applications where low-cost, wireless, multi-parameter, single-use, and multi-use medical and fitness and/or health devices are useful and beneficial. The wireless nature enables convenience, comfort and/or freedom of movement for the user and/or patient. The interaction between the single-use design and the multiple-use design allows for flexibility in the user's situation. In medical applications, the single use design reduces the risk of cross-contamination and infection associated with healthcare, simplifies workflow, and eliminates failures due to equipment wear and tear. In fitness and wellness applications, multi-use designs enable a more affordable solution for personal use. Embodiments of monitoring techniques may be applied to several clinical contexts, as well as fitness and wellness applications, including: pulse oximetry for COPD, anesthesia, flight and exercise, and oxygen therapy; noninvasive continuous blood glucose monitoring for diabetes disease management; continuous body temperature monitoring; monitoring ECG samples; electroencephalographic (EEG) continuous monitoring; non-invasive monitoring of water in blood for body hydration management; non-invasive total hemoglobin monitoring for anemia and/or blood infusion management; continuous hemoglobinemia monitoring, and the like.

In one aspect, a monitoring device includes: a light sensor, a temperature sensor, a first electrical contact sensor, and a second electrical contact sensor within a housing of the monitoring device. The light sensor includes: a light source and a photodetector disposed adjacent the first surface of the housing. The light source is operable to emit light towards a measurement location of a first body part of a user when the first surface is in contact with the measurement location, and the photodetector is operable to receive light reflected from the measurement location. The temperature sensor is disposed adjacent the first surface of the housing and is operable to measure a temperature at the measurement location when the first surface is in contact with the measurement location. The first electrical contact sensor is disposed adjacent the first surface of the housing to contact the measurement location when the first surface is in contact with the measurement location. The second electrical contact sensor is disposed adjacent to a second surface of the housing. The first electrical contact sensor and the second electrical contact sensor detect cardiac signals when a different second body part of the user contacts the second electrical contact sensor. The monitoring device further comprises: a wireless communication device operable to communicate the signals received from the photodetector, the temperature measurements, and the cardiac signal to an application on a host device.

In another aspect, a system includes: applications on the monitoring device and the host device. The monitoring device includes: a light sensor, a temperature sensor, a first electrical contact sensor, and a second electrical contact sensor within a housing of the monitoring device. The light sensor includes: a light source and a photodetector disposed adjacent the first surface of the housing. The light source is operable to emit light towards a measurement location of a first body part of a user when the first surface is in contact with the measurement location, and the photodetector is operable to receive light reflected from the measurement location. The temperature sensor is disposed adjacent the first surface of the housing and is operable to measure a temperature at the measurement location when the first surface is in contact with the measurement location. The first electrical contact sensor is disposed adjacent the first surface of the housing to contact the measurement location when the first surface is in contact with the measurement location. The second electrical contact sensor is disposed adjacent to a second surface of the housing. The first electrical contact sensor and the second electrical contact sensor detect cardiac signals when a different second body part of the user contacts the second electrical contact sensor. The monitoring device further comprises: a wireless communication device operable to communicate signals received from the photodetector, temperature measurements, and the cardiac signal. The application program on the host device is operable to process signals transmitted by the monitoring device to calculate a physiological parameter, waveform data associated with the physiological parameter, and trend data associated with the physiological parameter and cause at least one of the physiological parameter and a waveform or trend associated with the physiological parameter to be displayed.

In one embodiment, the monitoring device is a separate device that is attached to the user's measurement location. In another embodiment, the monitoring device is incorporated into a hat (e.g., a baseball cap) worn by the user. The cap may include built-in electroencephalogram (EEG) electrodes. Alternatively, the monitoring device is incorporated into a patch attached to the measurement location. The cap and the patch may include: circuitry that processes signals, wirelessly transmits signals to a host device, and performs operations such as power management and energy harvesting.

The monitoring device may obtain measurements for various physiological parameters continuously or at selected times and communicate the measurements to a host device. An application on the host device may display in a user interface or screen of the application a meter for one or more physiological parameters, a thermometer, one or more waveforms and/or trend waveforms or graphs. The application may generate an alert when the physiological parameter exceeds an upper limit and/or falls below a lower limit. The upper and lower limits may be set in a user setting interface of the application.

In some aspects, the battery uptime of a battery in the monitoring device may be estimated by the application on the host device, by the monitoring device, or in a distributed process using both the host device and the monitoring device. The calculations are performed via different functions in both closed and open loops. In closed loop, the battery voltage and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.) are used to directly estimate the battery charge. In open loop, the battery charge is estimated directly using one or more counters and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.).

Drawings

Non-limiting and non-exhaustive examples are described with reference to the following figures. The elements of the drawings are not necessarily to scale relative to each other. Identical reference numerals have been used, where appropriate, to designate identical features that are common to the figures.

FIG. 1 is a block diagram illustrating an example of a monitoring device connected to a computing device and a network;

2A-2F depict example measurement locations on a patient's body where a monitoring device may be applied;

2G-2L illustrate example adhesive tape layouts suitable for use with a monitoring device;

2M-2O depict example measurement locations on a patient's body where a monitoring device may be applied;

FIG. 3 depicts an example of a modulation scheme suitable for use with a monitoring device;

4A-4C illustrate examples of another modulation scheme suitable for use with a monitoring device;

FIG. 5 is a cross-sectional view of an example of a monitoring device;

6A-6C are schematic diagrams depicting examples of monitoring devices;

FIG. 7 is a flow diagram illustrating an example method of processing measurement data received from a monitoring device;

FIG. 8A depicts an example embodiment of a monitoring device having a light sensor;

FIG. 8B illustrates an example embodiment of a monitoring device having a temperature sensor and a light sensor;

FIG. 8C depicts an example embodiment of a monitoring device having a light sensor and an electrical contact sensor;

FIG. 8D illustrates an example embodiment of a monitoring device having an optical sensor, an electrical contact sensor, and a temperature sensor;

FIG. 8E depicts an example embodiment of processing circuitry configured to process signals received from the temperature sensors and/or electrical contacts shown in FIGS. 8A-8D;

FIG. 9A depicts an application program operating with a monitoring device installed in a host device;

9B-9C illustrate a monitoring device and an accessory;

FIG. 9D shows in detail the step of activating the monitoring device;

9E-9I depict a workflow showing several exemplary embodiments for attaching a monitoring device to various measurement locations;

9J-9Q illustrate the steps of starting an application in a host device, connecting a monitoring device to the host device, and initiating a continuous data exchange between the monitoring device and the host to produce measurements and waveforms;

9R-9T depict exemplary embodiments for a wearable fitness/medical cap;

fig. 9U depicts an exemplary embodiment for a wearable fitness/medical patch;

10A-10B depict a workflow for sharing data collected, processed, and analyzed by a host device working in combination with medical, fitness, and health device technologies;

10C-10L illustrate exemplary charts having data analysis based on data collected from users wearing medical, fitness, and health device technologies;

10M-10N illustrate exemplary file formats for storing trends and encoded data format waveforms;

FIGS. 10O-10P illustrate in detail the steps for calculating the log and wave rate of the measurement data;

FIGS. 10Q-10R are detailed views showing steps for calculating Ln logarithmic fluctuation ratio and Ln fluctuation ratio of the measured data;

11A-11B illustrate embodiments of generating identification and hardware diagnostic parameters by an application on a host device;

FIG. 11C depicts an example embodiment relating to sharing measurement data with a technical support team of monitoring devices;

FIG. 12A depicts an example embodiment of an alarm/warning system;

FIG. 12B shows an example setup screen for an application;

FIG. 13A depicts a battery icon and an example battery status;

FIG. 13B shows an example battery discharge curve;

FIG. 13C depicts an example method of estimating battery uptime of a battery in a monitoring device;

FIG. 13D depicts a flow diagram of a tamper-resistant method for monitoring a device;

FIG. 14A depicts a first example method of determining an effective noise floor of a monitoring device;

FIG. 14B illustrates a second example method of determining an effective noise floor of a monitoring device;

FIG. 15A depicts an example battery discharge curve;

15B-15C illustrate an example method of estimating battery uptime of a battery in a monitoring device; and

fig. 16 shows a flow chart of a method of operating a monitoring device.

Detailed Description

As used herein, the term "optimal" is intended to be interpreted broadly, and is intended to cover providing optimal, substantially optimal values and models, as well as acceptable values and models. As used herein, the term "data stream" refers to data that is sequentially indexed by exogenous quantities (e.g., temporal, spatial, etc.). For example, the data stream as a function of time is assumed to be indexed by time, such as a discrete time system. The data stream as a function of space is assumed to be indexed by space. Depending on the application, the data stream may be indexed by quantities that have a physical meaning or are substantially abstract. The embodiments disclosed herein may be applied to any data stream regardless of its indexing or sampling method.

Reference will now be made in detail to the exemplary embodiments illustrated in the accompanying drawings. It should be understood that the following description is not intended to limit the described embodiments to the described example embodiments. As used herein, a monitoring device is an electronic fitness or monitoring device that measures, tracks, and/or reports data regarding the measurement of one or more physiological parameters (including but not limited to heart rate, blood perfusion, oxygen saturation, body temperature, etc.). The measurements or data related to the measurements are sent to a computing device for further processing. For example, oxygen saturation (SpO2), Pulse Rate (PR), and Perfusion Index (PI) may be estimated on a computing device.

Fig. 1 is a block diagram illustrating an example of a monitoring device connected to a computing device and a network. The monitoring device 100 is attached to one or more measurement locations from which the sensors can easily access blood perfusion information. In the illustrated embodiment, the measurement location 111 is a finger or digit (see also fig. 2A). Other example measurement locations include, but are not limited to: the patient's temples (fig. 2B), forehead (fig. 2C), neck (fig. 2D), arms (fig. 2E and 2F), ears or earlobes (fig. 2M), nose (fig. 2N), and/or posterior pinna of the ears (fig. 2O).

In one embodiment, the monitoring device 100 includes: processing device 102, instrument circuitry 107, communication device 103, and storage device 115. The instrument circuitry 107, communication device 103, and storage device 115 are connected to the processing device 102. The converter 108 is connected to the instrument circuitry 107 and the switch circuitry 112. The communication device 103, storage device 115, processing device 102 and instrument circuitry 107, and power supply 109 are connected to switching circuitry 112. The monitoring device 100 may further include: adhesive tape for attaching the monitoring device 100 to a measurement location.

The monitoring device 100 may be turned on using the switching circuitry 112. In one example, the switching circuitry 112 is a single-use conductive strip switch. The instrument circuitry 107 may include: one or more light sources, e.g., Light Emitting Diodes (LEDs); control circuitry and logic; and one or more photodetectors, such as photodiodes. The communication device 103 may be any suitable type of communication device including, but not limited to, a wireless low power radio (examples of which include, but are not limited to BLE, ANT, Zigbee, etc.). Wireless connection and authentication between the communication device 116 and the communication device 103 (when needed) may be accomplished by standard pairing methods (i.e., Just-in-function (Just Works), etc.) and out-of-band methods (e.g., Near Field Communication (NFC), barcode/image scanning, or via an optical link between the optical sensor 110 and a camera (or optical sensor) housed in the computing device 105). Depending on the configuration of the monitoring device 100, the storage device 115 may include, but is not limited to: volatile storage (e.g., random access memory), non-volatile storage (e.g., read only memory), flash memory, or any combination of these.

The monitoring device 100 may be communicatively coupled to a computing device 105, such as a smart phone, a tablet computing device, a desktop or laptop computer, a wireless computing and/or data aggregator application device, a bedside monitor, or similar computing device through a wired or wireless connection. Computing device 105 may include: a communication device 116 connected to the processing device 117 and a storage device 118. The monitoring device 100 transmits the measurement data to the computing device 105 via the communication device 103 (via the communication device 116) for processing, display and/or storage. The measurement data may be used for alerts for electronic medical record data transfer, for data sharing, and/or other uses of the data.

Computing device 105 may also include one or more input devices (represented by input device 121) and/or one or more output devices (represented by output device 122). An input device 121 and an output device 122 are connected to the processing device 117. Input device 121 may be implemented as any suitable input device, such as: a keyboard (physical or virtual), a mouse, a trackball, a microphone (for voice recognition), an image capture device and/or a touch screen or touch display, or any other computer-generated sensory input information. Output device 122 may be implemented as any suitable output device, such as: a display, one or more speakers and/or a printer, or any other computer-generated sensory output information. In some embodiments, the measurement data or data representing the measurement data may be provided to the output device 122. For example, the measurement data or data representing the measurement data may be displayed on a display.

In some embodiments, the computing device 105 and/or the monitoring device 100 may access the external storage device 119 over one or more networks (represented by network 120) to store and/or retrieve measurement data. In one or more embodiments, network 120 is illustrative of any suitable type of network, such as an intranet and/or a distributed computing network (e.g., the internet) over which computing devices and/or monitoring device 100 may communicate with other computing devices.

As will be described in greater detail later, measurement data generated by the monitoring device 100 may be processed to determine or estimate one or more physiological parameters (e.g., pulse rate, blood oxygen saturation). As part of the processing, one or more signals are processed using a numerical solver device. The numerical solver device may be implemented using one or more circuits (circuitry), software algorithms or programs executed by one or more processing devices (e.g., processing device 102 and/or processing device 117), or a combination of circuitry and software algorithms.

For example, in one embodiment, the storage device 115 in the monitoring device 100 may include several software programs or algorithms and data files, including a numerical solver device. When executed on processing device 102, the numerical solver device may perform and/or cause to be performed processes including, but not limited to, aspects described herein. In another embodiment, storage device 118 in computing device 105 may include several software programs or algorithms and data files, including a numerical solver device. When executed on processing device 117, the numerical solver device may perform and/or cause to be performed processes including, but not limited to, aspects described herein. In some other embodiments, the operations of the numerical solver device are distributed such that some of the operations are performed by processing device 102 and some of the operations are performed by processing device 117.

Fig. 2G-2L illustrate example adhesive tape layouts suitable for use with a monitoring device. In the figures, each monitoring device uses a different adhesive tape layout, such as those depicted in fig. 2G and 2H, respectively. Specifically, in the illustrated embodiment, the monitoring device 203 is attached to the patient's fingertip 202 using a flat adhesive bandage 205 enclosed in a Polytetrafluoroethylene (PTFE) bag or a paper fold made of biocompatible tape 206, as shown in fig. 2G. When the lower face 208 of the monitoring device 203 is attached to the skin of the patient, the light sensor 207 on the lower face 208 may contact the skin of the patient. In some of many alternative embodiments (e.g., those shown in fig. 2H-2L), an adhesive bandage or tape 204 may be used to attach the monitoring device 203 to the measurement location.

Small footprint

Embodiments of the monitoring device may provide a smaller footprint (size). Smaller dimensions may require less material in manufacture, easier use, less storage space required, less transportation costs, less invasive equipment, and instruments for the patient that are more comfortable and more portable in using the monitoring device, among other things. In one embodiment, the monitoring device 100 may include: a Printed Circuit Board (PCB) including a processing device 102 with an integrated communication device 103; a compact integrated circuit including instrumentation circuitry 107 for signal conditioning and LED current drive; a power supply 109; and a converter 108. The power supply 109 in combination with the converter 108 provides the higher voltages needed to drive the light source 113 and/or the photodetector 114 of the light sensor 110. In one embodiment, the converter 108 is a single DC-DC switching converter, the power supply 109 is a disposable battery, the light source 113 is an LED, and the photodetector 114 is a silicon photodiode.

The processing device 102 and the instrument circuitry 107 may be powered directly by the power supply 109. The light sensor 110 may be packaged with the PCB by any of a number of suitable devices and methods, including, by way of example, by attaching various types of flexible adhesive tape (optionally in combination with PTFE) to the PCB. Those skilled in the art will appreciate that a PCB may be rigid or flexible, or in the form of a substrate, where some or all of the components are a die attached and wire bonded to the substrate, and encapsulated for protection with epoxy or some other encapsulation material. Further, the light sensor 110 can be attached to the measurement location 111 using any of a number of suitable devices and methods, including, by way of example, by using an adhesive tape as part of the light sensor 110 packaging structure (as described herein).

Low power consumption

In some aspects, the processing device 102 is a low power ARM processor with dual functionality for controlling a wireless low power radio (communication device 103) and instrument circuitry 107. The light sensor 110 may include: a high efficiency LED and at least one silicon photodiode arranged in a reflective configuration such that the LED and the at least one silicon photodiode are physically separated from each other to minimize required LED current and front end gain in the instrument electronics. The instrument circuitry 107 may have a very low bias current and operate at a low voltage. In one embodiment, ambient light interference may be avoided or at least reduced by modulating and time multiplexing the current of the LEDs at higher frequencies to shift the spectral content of the generated light signal and the detected light signal into a range of the frequency spectrum where ambient light interference is less likely to occur.

Fig. 3 depicts a distributed system suitable for processing signals generated by a monitoring device and an example modulation scheme suitable for use with the monitoring device. The modulation scheme 300 may reduce the complexity of the demodulation, extraction, LED current calibration, patient sensing (sensor off), error handling and alarm, diagnostic and/or communication algorithms shown in the algorithm block 302. Some or all of the blocks in the algorithm block 302 are included in the monitoring device 303. The LED driver algorithm, front end algorithm, and supervisory algorithm may all be software programs stored in the storage device 115.

In the modulation scheme depicted in fig. 3, each LED (light source 113) is kept on for approximately 25% of the modulation time period (LED duty cycle). Smaller LED duty cycles can be used to reduce overall power consumption. The LED may remain off for approximately 50% of the modulation time period. The interval during which the LED is turned off may also be increased if the LED duty cycle is to be reduced and if the modulation frequency remains the same. Two time slots 305, 306 in the waveform represent the time when the LED is turned off. Two time slots 305, 306 may be used to detect and eliminate the effects of ambient light. In an embodiment, which may use as low a modulation frequency as 1KHz to make the signal-to-noise ratio data similar to a medical-grade pulse oximeter, complex filtering and signal processing in a demodulation scheme is performed to recover the optical signal generated by the interaction of the LED optical signal with the attenuation caused by the tissue perfusing the blood oxygenation at the measurement site.

In some embodiments, a distributed computing architecture may be used to compute one or more physiological parameters, such as blood oxygen saturation (SpO2), Pulse Rate (PR), and Perfusion Index (PI). For example, SpO2, PR, and PI are evaluated on a host computing device 304 (e.g., a mobile phone or laptop computer) to increase the battery life of the monitoring device. In one embodiment, one or more numerical solver devices can also be included in the back-end algorithm of the host computing device 304. For example, the numerical solver device can also be included in the oxygen saturation and pulse rate algorithms, as well as the perfusion index algorithm. In another example, the one or more numerical solver devices can be separate algorithms that are invoked by the oxygen saturation and pulse rate algorithms and by the perfusion index algorithm.

In other embodiments, one or more numerical solver devices may also be included in monitoring device 303. For example, one or more numerical solver devices can be implemented in a front-end algorithm, such as, for example, a demodulation algorithm.

A processing device (e.g., processing device 102 in fig. 1) in monitoring device 303 may perform time-critical, high-frequency, low-latency, and low-complexity tasks. Data processed by the processing device in the monitoring device 303 may be reduced in bandwidth by a decimation algorithm and wirelessly transmitted to the host computing device 304 (e.g., to the processing device). In one embodiment, the host computing device 304 may perform more complex, high latency tasks to calculate and continuously display the measured values of SpO2, PR, and PI.

In an example embodiment, a monitoring device front end (AFE4403) from texas instruments may be used as instrument circuitry 107 (fig. 1). In such embodiments, the monitoring device front end may be programmed to directly generate and control the required LED modulation scheme without additional resources from the sensor processing device 102.

Other example modulation schemes are shown in fig. 4A. The RED-GREEN-IR modulation scheme and/or the multi-wavelength sequential modulation scheme may be used in conjunction with measurement locations that have low perfusion and/or experience excessive motion. Fig. 4B and 4C depict a flow chart of a method of determining which modulation scheme to use. Fig. 4B-4C illustrate example scenarios in which a particular type of modulation may be advantageous. In the method illustrated in fig. 4B, the modulation scheme employed depends on the aforementioned factors (e.g., low perfusion and/or experiencing excessive motion). Initially, as indicated at block 400, it is determined whether the measurement location has low perfusion and/or experiences motion. If not, the process passes to block 402, where the modulation scheme shown in FIG. 3 may be used. When the measurement site has low perfusion and/or experiences motion, the method continues at block 404, where a RED-GREEN-IR modulation scheme or a multi-wavelength sequential modulation scheme may be used.

In the method shown in fig. 4C, it is determined at block 406 whether one or more measurements of other blood parameters are to be obtained or determined. The blood parameters may include, but are not limited to, glucose, water, and hemoglobin. If one or more measurements of other blood parameters are to be determined, the process passes to block 408, where the modulation scheme shown in FIG. 3 or the RED-GREEN-IR modulation scheme shown in FIG. 4A may be used. If one or more measurements of other blood parameters are not to be determined, the method continues at block 410 where a multi-wavelength modulation scheme may be used.

For the RED-GREEN-IR modulation scheme shown in fig. 4A, the GREEN LED and the RED LED are activated and modulated for a period of time according to the above-described on-off pattern, and then the RED LED (RED) is replaced with the near-infrared LED (IR) and also modulated for a period of time. The sequence of events repeats itself while the measurement site experiences motion and/or low perfusion levels. When light in the wavelength range between violet and yellow (i.e. between about 400nm and 590 nm) is applied to the measurement site of perfused blood, more light scattering and absorption is seen in this region, creating a photoplethysmogram (photoplethysmograph) that is much larger in amplitude when compared to the amplitude in the red and near infrared wavelength regions. Green wavelengths are commonly used because LEDs in this range provide good efficiency and reliability and lower cost when compared to other wavelengths in the violet-yellow range. Furthermore, the optical properties of blood in the green region are desirable in terms of scattering and absorption levels. The photoplethysmograph associated with the green LED may be used to improve detection of heart rate and/or detection of red and near infrared true photoplethysmograph amplitudes and waveforms, which is required for accurate measurement of oxygen saturation of blood in hypo-perfused and motor conditions.

The multi-wavelength sequential modulation scheme shown in fig. 4A may be used in some embodiments where the parameter of interest requires wavelengths other than red and near-infrared LEDs. Examples include non-invasive measurement of other blood components (parameters) (e.g., glucose for diabetes management, water for body hydration management, total hemoglobin for anemia and/or blood infusion management, etc.). As shown in fig. 4A, several light sources of different center wavelengths (i.e., λ 1, λ 2, …, λ n LEDs) are turned on and off sequentially in time. In the case of non-invasive measurement of glucose, a plurality of LEDs in the range of 900nm to 1700nm may be employed. In the case of non-invasive measurement of total hemoglobin and/or water, wavelengths in the range of 600nm to 1350nm should be sufficient. The defined spectral range is sufficient because the blood component and the blood-free component at the measurement location have spectral characteristics that are usually quite different depending on the wavelength subrange under consideration. For example, water and glucose have higher absorption in the 1550nm to 1700nm range than other components, hemoglobin species have significant features in the 600nm to 1350nm range, fats generally have significant scattering properties over the entire range when compared to other blood components, and so on. The modulation schemes shown in fig. 3 and 4A may be switched in time depending on the particular application and/or measurement scenario.

In some embodiments, the method shown in FIG. 4C may be used in a multi-parameter monitoring device that continuously measures SpO2, PR, and PI using the modulation shown in FIG. 3 and/or the RED-GREEN-IR modulation scheme shown in FIG. 4A. The monitoring device may perform low frequency periodic sample measurements of other blood parameters, such as those mentioned previously (i.e., glucose, water, etc.), using a multi-wavelength modulation scheme. This topology (topology) is possible because the concentration of water, glucose, hemoglobin, etc. in blood typically changes slowly when compared to SpO2, PR, and PI. Because typical measurement periodicity for the parameters is generally much longer (i.e., once every 30 minutes, once every hour, etc.), the increase in power consumption by the monitoring device is not significant. The additional LED and photodetector technology required (i.e., silicon and indium gallium arsenide photodiodes for the 600nm to 1700nm wavelength measurement range) represents a small cost increase and a negligible increase in sensor footprint.

The multi-wavelength modulation scheme shown in fig. 4A can also be used to measure SpO2, PR, and PI. In this configuration, the red and near infrared LEDs are combined with other wavelengths to create an "n" photoplethysmogram, which can be used to improve SpO2 accuracy or motion performance. Accuracy is at least partially improved because additional LEDs across the visible and near infrared range enable the estimation algorithm to cope with the optical interference effects of other and non-blood components that are not needed in the measurement of oxygen saturation, pulse rate and/or perfusion. Operation under motion is improved because the effect of motion acceleration on venous and capillary blood creates optical disturbances in the measurement location that have different morphological characteristics according to wavelength range and are therefore more likely to be eliminated from the photoplethysmogram by advanced signal processing such as the numerical solver device described herein.

Those skilled in the art will appreciate that the wavelengths and other measurements and ranges discussed herein are generally intended as representative of certain embodiments of the invention and are not intended as limitations on the many ways in which the invention may be practiced.

As previously described, the distributed computing architecture may be used to compute SpO2, PR, and PI, where SpO2, PR, and PI are evaluated at host computing devices (e.g., host computing device 304 in fig. 3 and computing device 105 in fig. 1) to increase battery uptime of the monitoring device. A processing device (e.g., processing device 102 in fig. 1) in the monitoring device may perform time-critical, high-frequency, low-latency, and low-complexity tasks. Data processed by a processing device in the monitoring device may be reduced in bandwidth by a decimation algorithm and wirelessly transmitted to the host computing device. In one embodiment, one or more processing devices in the host computing device (e.g., processing device 117 in computing device 105) may perform more complex, high-latency tasks to calculate and continuously display the measured values of SpO2, PR, and PI.

FIG. 5 is a cross-sectional view of an example monitoring device. Fig. 5 depicts one of many ways to make a stack-up of the components of a wireless disposable continuous monitoring device 500. A PTFE envelope or paper fold made of biocompatible tape 510 may house the components of the monitoring device 500. From top to bottom, the monitoring device 500 may include: an antenna 509, a battery 508, a Printed Circuit Board (PCB)501 and PCB circuitry 502, and a light sensor 503. For attachment to a measurement location of a patient, such as a fingertip, the monitoring device 500 may include a PCB to skin adhesive layer 506. The adhesive layer 505 is made of conductive tape (e.g., isotropic pressure sensitive conductive tape) and the adhesive layer 504 contains electrical contacts that (when closed) feed power to the PCB 501. An activation pad 507 may be provided between the adhesive layer 504 and the adhesive layer 505, and on the adhesive layer 506, such that when the activation pad is removed, the light sensor 503 and the adhesive layer 506 are exposed for attachment to a measurement site of a patient, and the layer 504 and the layer 505 are connected to provide power on the monitoring device.

Fig. 6A-6C are schematic diagrams illustrating an example monitoring device. The monitoring device may include an integrated circuit 602 (fig. 6A), such as AFE4403 circuitry of texas instruments or AFE4490 circuitry, including a photodiode front end, LED drivers, and control logic. The light sensor 603 (fig. 6A), e.g., the SFH7050 sensor of OSRAM, may include green, red, and near infrared LEDs and silicon photodiodes. The monitoring device may include a main processing device 601 (fig. 6B), such as an ARM Cortex M0 processor available from nordic semiconductors. In addition, the monitoring device may include a 16MHz crystal oscillator 605, a 32.768kHz crystal oscillator 604 (when ANT low power radio is used), a 2.45GHz impedance balloon filter 606 (single to differential), an impedance matching circuit 607, and an antenna 608 (fig. 6B). The power management circuit of the monitoring device shown in fig. 6C may include: a boost converter 621, e.g., TPS61220 from texas instruments; a ferrite inductor 611; boost converter voltage setting resistors 609, 610; a debug pin 612 for the main processing device 601; a noise suppression pull-down resistor 613; a battery voltage terminal 614; turn on the switch pin 615; and voltages 616, 617, 618, 619, 620 for the main processing device 601 (fig. 6B) and the integrated circuit. In one embodiment, the on switch pin 615 is a single use pin.

As will be appreciated, the components depicted in fig. 6A-6C, and the corresponding descriptions of fig. 6A-6C, are for illustrative purposes only and are not intended to limit embodiments to a particular order of steps or a particular combination of hardware components or software components.

FIG. 7 is a flow diagram illustrating an example method of processing measurement data received from a monitoring device. The illustrated method fits the measurement data to a model and, based on the model, determines one or more physiological parameters (e.g., PR, SpO2, PI). Depending on the application, the method of fig. 7 is performed once, or the method is repeated a given number of times. For example, with a monitoring device, the method shown in fig. 7 may be repeated as long as a stream of measurement data is received. In a non-limiting example of a monitoring device, the method of FIG. 7 is repeated substantially every 0.75 seconds.

Initially, as shown at block 700, a stream of measurement data is received. In one embodiment, the stream of measurement data is a digital stream of time-multiplexed and modulated measurement data. In a monitoring device embodiment, the flow of measurement data represents any suitable number of measurement samples captured by the monitoring device at a given sampling frequency (e.g., 4 kHz). In one embodiment, the stream of measurement data is continuously captured by the monitoring device, but other embodiments are not limited to such an implementation.

The stream of measurement data is then demodulated and filtered at block 702 to produce a respective data stream for each wavelength channel (e.g., red, infrared, etc.). Any suitable demodulation technique may be used. In a non-limiting example embodiment, a demodulation system may include: a multi-channel symmetric square wave demodulator device operable for connection to a filter device as disclosed in co-pending us application 16/198,550 filed on 21.11.2018. The filter device may be implemented as a single stage or multi-stage filter device. In some embodiments, the demodulator device and/or the filter device perform decimation, wherein the sampling frequency is reduced to a lower value (e.g., from 4kHz to 1kHz, from 1kHz to 50Hz) to reduce signal processing requirements, wireless bandwidth, and/or power consumption. Additionally or alternatively, the demodulation systems and techniques can remove most or substantially all of the interfering signals within a predefined continuous frequency range (i.e., 0Hz to 800 Hz).

In some aspects, each respective data stream is a photoplethysmogram data stream. At block 704, each respective data stream is normalized. In one embodiment, the logarithm of each data stream is taken and band-pass filtered to produce a photoplethysmogram data stream for each wavelength channel.

Next, at block 706, the photoplethysmogram data stream is processed by a numerical solver device to calculate or estimate optimal variables that minimize a cost function to produce one or more photoplethysmogram models. In one embodiment, the photoplethysmogram data stream is processed in batches of arbitrary size as appropriate for the particular application. For example, with a monitoring device, the batch data size may be equal to a few seconds of data (e.g., 250 samples collected over 5 seconds) and updated in real-time every specific time interval (e.g., 0.75 seconds).

In one aspect, a numerical solver may compare the data stream to a sequence of indexed photoplethysmographic models parameterized by optimal variables. For example, for each Pulse Rate (PR) value from 25BPM to 250BPM in steps of 1BPM, the numerical solver device calculates the value of the optimum variable that minimizes the cost function to produce the optimum photoplethysmographic model for the given data stream. As disclosed in co-pending U.S. application 16/198,504 filed on 21/11/2018, in one embodiment, the cost function may be defined by the following formula:

wherein A ∈ R^k×mK is equal to or greater than m, is a constant matrix, b_i∈R^kN is a constant vector, x ∈ R^mAnd z ═ z₁z₂ … zn]^T∈RⁿIs the optimal variable vector and the T superscript is the transpose operator.

Each photoplethysmogram model generated based on the numerical solver device and its corresponding PR value is considered as a data point (pair). Thus, in this example, the photoplethysmography model is indexed by PR values. If the cost function is given by equation 1, then each photoplethysmogram model is given by a vector Ax and a scaling factor z_iIs represented and the photoplethysmogram data stream is represented by vector b_iAnd (4) showing. The optimal variables are the vector x and the scaling factor z_i. Each column in matrix a provides information about a potential application or phenomenon. In one embodiment, the matrix a is indexed by (a function of) the PR values. Thus, the entries in matrix A change for each PR value, which in turn changes for x and the scaling factor z_iMinimizes the optimal solution of the cost function in equation 1.

Next, as shown in block 708, one or more metrics are calculated for each photoplethysmogram model indexed by PR value by comparing the photoplethysmogram model to a reference photoplethysmogram model. The reference photoplethysmogram model represents an optimal or selected photoplethysmogram model for a user associated with the measurement data. In one embodiment, the one or more metrics are associated with a photoplethysmogram model. Example metrics include, but are not limited to: root mean square accuracy (Arms), correlation values, L2 norm, L1 norm, Linf norm, power, correlation values, and harmonic and morphological analysis matches. Because one or more metrics are calculated based on the photoplethysmograph model indexed by PR values, one or more metrics are also indexed by the same PR value.

For example, in some embodiments, the one or more calculated metrics are compared to corresponding metrics (reference metrics) associated with the reference photoplethysmogram model to determine how close or similar the one or more calculated metrics are to the corresponding reference metrics. Additionally or alternatively, the shape of each photoplethysmogram model is compared to the shape of the reference photoplethysmogram model to determine how similar or dissimilar each photoplethysmogram model is to the reference photoplethysmogram model. In some embodiments, metrics such as root mean square accuracy (Arms), correlation values, L2 norm, L1 norm, Linf norm, correlation values, and harmonic and morphological analysis matching may be used to derive a degree of conformance (shape similarity) between the photoplethysmogram model and the reference photoplethysmogram model.

At block 710, an optimal photoplethysmogram model for each wavelength channel and one or more values of interest for estimation or calculation is selected or determined. The values of interest may include: a value of interest of a physiological parameter (e.g., SpO2, PR, PI, and/or other physiological parameter of interest). The value of interest is calculated by applying classification criteria (algorithms) to the calculated metrics (i.e., maximum, minimum, ratio of values, linear and non-linear classification algorithms, etc.). For example, the best estimate for PR for a given red and infrared data streams may be obtained by choosing a PR value that yields the photoplethysmogram model with the greatest normalized power as long as the corresponding photoplethysmogram model Arms error value (when compared to the most recent reference photoplethysmogram model) is less than a specified threshold. The best estimates for SpO2 and PI may be calculated via scaling factors (red and infrared amplitudes) based on the photoplethysmogram model that yields the best estimate for PR.

One or more outliers are then removed from the estimated values of interest to produce a subset of the values of interest. In some embodiments, an average estimate of the value of interest is generated at block 712. Any suitable technique may be used to remove outliers.

The subset of values of interest is then provided to a storage device (e.g., memory) and/or an output device (block 714). For example, one or more values of interest may be displayed on a display. Next, as described in block 716, the reference photoplethysmogram model is updated based on the subset of values of interest and/or the optimal photoplethysmogram model (e.g., associated optimal variables). In one embodiment, the reference photoplethysmogram model is updated via an update rule that produces a weighted average of the current reference photoplethysmogram model and the optimal photoplethysmogram model.

Fig. 8A-8D depict example embodiments of low cost single use monitoring devices. In fig. 8A, the monitoring device 100 is fixed to a measurement location 804 and includes a light sensor 110. As previously described, the light sensor 110 includes one or more light sources 113 and one or more photodetectors 114. The one or more light sources 113 emit light 807, the light 807 penetrating the epidermis 800 and the blood oxygen infused dermis 810 and interacting with the pulsatile signal of the heart and creating a light pulsatile signal. The light pulse signals (photoplethysmographs) are captured by front end circuitry (e.g., instrument circuitry 107 and processing device 102 in fig. 1) connected to one or more photodetectors 114. The front end circuitry filters, conditions and/or converts the optical pulse signal to a digital signal. These digital signals may be wirelessly transmitted to a host device (e.g., host device 105 using communication device 103) for further processing and analysis, real-time measurement display, alert (alarm) generation, and/or storage of the subject's SpO2, PR, PI, etc.

In fig. 8B, the monitoring device 100 includes a light sensor 110 and one or more temperature sensors (represented by temperature sensor (T) 801) in contact with a measurement location 804. The temperature sensor 801 is used to measure the core body temperature. For example, the monitoring device 100 may be attached to the forehead, the ear (posterior auricle), or some other location on the body of the subject that has a surface temperature that is related to the core temperature of the body. The temperature sensor 801 may be connected to temperature front end circuitry 808 (depicted in fig. 8E) that conditions, filters, and/or converts the temperature signal into a digital temperature signal. The digital temperature signal may then be wirelessly transmitted to a host device (e.g., host device 105 in fig. 1) for further processing and analysis, real-time measurement display, alert (alarm) generation, and/or storage of the body's core temperature. In one embodiment, the temperature front end circuitry 808 includes the instrument circuitry 107 and the processing device 102 shown in fig. 1.

The monitoring device 100 in fig. 8C includes a first electrical contact sensor (E1)802 and a second electrical contact sensor (E2) 803. This example embodiment may be used in measurement locations where the temperature sensor (T)801 is not required, or where the surface temperature at the measurement location is not related to the core temperature of the body. In the illustrated embodiment, the monitoring device 100 is attached to a measurement location 804 (e.g., a finger of a body). A first electrical contact sensor (E1)802 is provided on an inner side of a first surface of the monitoring device 100, wherein an outer side of the first surface is in contact with the measurement location when the monitoring device is attached to the measurement location. Thus, when the monitoring device 100 is disposed at the measurement location, the first electrical contact sensor (E1)802 is in continuous contact (or near continuous contact) with the measurement location.

A second electrical contact sensor (E2)803 is provided on the inner side of the second surface of the monitoring device 100. In fig. 8C, the second surface is opposite to the first surface, but other embodiments are not limited to this configuration. The second electrical contact sensor (E2) may be located on one side of any surface such that the second electrical contact sensor (E2)803 does not contact the measurement location when the monitoring device 100 is attached to the measurement location.

When the monitoring device 100 is placed on or attached to the measurement location 804, the subject may touch the electrical contact sensor 803 with the finger 805 of the hand on the other side to create a closed electrical path to the heart 806 of the subject and enable measurement of the subject's electrical heart activity (e.g., Electrocardiogram (ECG)). The first electrical contact sensor 802 and the second electrical contact sensor 803 may be connected to an ECG front end circuitry 809 (depicted in fig. 8E). The ECG front end circuitry 809 conditions, filters and/or converts the ECG signal to an ECG digital signal. The ECG digital signals can be wirelessly transmitted to a host device (e.g., host device 105 in fig. 1) for further processing and analysis, real-time display of measurement results, alert (alarm) generation, and/or storage of ECG signals. In one embodiment, the ECG front end circuitry 809 is part of the instrument circuitry 107 and includes the processing device 102 shown in fig. 1.

Referring to fig. 8D, this example embodiment includes a temperature sensor (T)801 and first and second electrical contact sensors 802 and 803. In the illustrated embodiment, the measurement location 804 is assumed to have a surface temperature related to the core temperature of the body (i.e., forehead, ear (posterior pinna), armpit, etc.) or to have a temperature that can be mapped to be related to the core temperature of the body (i.e., finger, etc.). Similar to fig. 8C, when the monitoring device 100 is placed on the measurement location 804, such that the first electrical contact sensor (E1)802 contacts the measurement location, and the subject touches the second electrical contact sensor (E2)803 with the finger 805 or any other body part that creates a closed electrical path to the subject's heart 806, the subject creates a closed electrical path to the subject's heart 806. In an embodiment where electrical isolation between the device 100 and the body of the subject is preferably monitored, the first electrical contact sensor 802 and the second electrical contact sensor 803 depicted in fig. 8C and 8D are replaced by capacitive contact sensors. In such embodiments, the ECG front end circuitry 809 may have a high input impedance to enable acquisition of an ECG of the heart without any low frequency harmonic distortion (attenuation) that may be created by capacitive contact sensors.

Fig. 9A depicts an application program that works with a monitoring device (hereinafter "application program") installed in a host device. In one embodiment, an application is downloaded from an application provider's online application site or website and installed in a host device. An icon 902 representing an application may be displayed on the screen 900 on the host device. For example, the monitoring device is an OXIOM pulse oximeter, and the application is the OXIOM application of True muscles, Inc. Example host devices include, but are not limited to, smart watches, desktop computers, laptop computers, and cellular phones or other mobile computing devices.

Fig. 9B-9C illustrate a monitoring device and an accessory. To prevent tampering with monitoring device 100 during storage and/or transport, tamper resistant label 903 seals package 904 until ready for use by a user. When the user is ready to use the monitoring device 100, the user tears off the tamper resistant label 903 and removes the product indicia 906 with the barcode. In fig. 9C, monitoring device 100 and accessories are removed from package 904. In this example, package 904 contains a roll of strap 907 and a headband 908 in addition to monitoring device 100 and product tag 906. In one embodiment, tape 907 is a self-adhesive micro-breathable tape. Headband 908 may be stored inside a roll of tape 907 and removed from the roll of tape 907 as the roll of tape 907 is removed from enclosure 904 (removal of headband 908 is represented by the arrow).

Fig. 9D shows the step of activating the monitoring device in detail. In step one, tab 909 labeled "1" is removed to close the electrical contact and monitor device 100 is turned on. In one embodiment, the tags 909 are made from conductive strips. In step two, an indicator 910 (e.g., a point) on the outer surface of the monitor device 100 is pressed by the user to confirm that the electrical contact is closed to open the monitoring device. When the monitoring device 100 is turned on, a light 905 (e.g., a green light) is turned on and indicates that the monitoring device 100 is capable of being operated for connecting to an application on a host device. In one example, the lights 905 may be part of the one or more light sources 113 of the light sensor to reduce cost and device footprint.

In step three, the label 911 labeled "2" is removed to reveal the adhesive tape used to attach the monitoring device 100 to the measurement location. For example, the adhesive tape can be a biocompatible adhesive tape. In one embodiment, the second electrical contact sensor (e.g., 803 in fig. 8D) is marker 906 depicted in step one of fig. 9D. Marker 906 may be made of a metal adhesive tape, thereby having the dual function of identifying monitoring device 100 and acting as a second electrical contact sensor. Additionally or alternatively, the first electrical contact sensor (e.g., 802 in fig. 8D) and the temperature sensor (e.g., 801 in fig. 8D) may be part of an adhesive tape that is revealed upon removal of the label 911. The adhesive tape, as part of the outer housing or casing of the monitoring device, may be designed to have good adhesion to the skin of the subject, as well as good electrical and thermal properties, to enable accurate measurement of the core temperature of the body and the ECG of the heart.

9E-9I depict flowcharts illustrating processes for attaching example monitoring devices to a measurement location. Fig. 9E illustrates a process of placing the monitoring device 100 on a user (e.g., to a user's digit). In the illustrated embodiment, steps one through three of fig. 9D have already been performed. The monitoring device 100 is attached to a measurement location 804 (e.g., a fingertip) with the light source 113 and the photodetector 114 of the light sensor (and the temperature sensor 801 and/or the first electrical contact sensor 802 if included in the allied device 100) placed on and in contact with the measurement location 804. In some embodiments, monitoring device 100 is wrapped with a band 907 to improve optical, electrical, and/or thermal coupling between monitoring device 100 and measurement location 804. Strap 907 may also protect monitoring device 100 from various events including, but not limited to, mechanical shock, displacement, and direct sunlight exposure.

However, in embodiments where the monitoring device 100 includes a first electrical contact sensor and a second electrical contact sensor (e.g., 802, 803 in fig. 8D), the strap 907 may cover the second electrical contact sensor (e.g., 803 in fig. 8D), depending on how the strap 907 is wrapped around the measurement location 804 and the monitoring device 100. Accordingly, conductive fibers (not shown) may also be included in the tape 907 to create a tape with conductive properties. The conductive fibers in the band 907 create an electrical contact between the second electrical contact sensor and the band 907. Whenever the subject touches the conductive auto-adhesive tape 907 with another body part (e.g., hand 805 on the other side in fig. 8D), a closed electrical path (e.g., 806 in fig. 8D) to the subject's heart is created. In some examples, the conductive fibers are arranged so as not to block wave frequencies in the usable wireless frequency range (e.g., a frequency range between 2400MHz and 2483.5MHz for bluetooth low energy) to enable the monitoring device 100 to wirelessly communicate with a host device.

In another embodiment, the strap 907 with conductive fibers may be made part of the monitoring device 100 to perform the function of raising the gain of the wireless antenna in the monitoring device 100. To increase wireless efficiency, when using a conductive strip 907 with conductive fibers, wrapping the strip 907 around the measurement location 804 may be done in a way that leaves a cavity (or hole, gap) 912 at the tip of the fingertip. The cavity 912 may be created based on how the strap 907 wraps around the monitoring device 100 and the measurement location 804 (e.g., a fingertip) and/or by the way the strap 907 is designed for and/or attached to the monitoring device 100.

The conductive fibers in band 907 may improve optical, electrical, and/or thermal coupling (contact) with measurement location 804 when pressure is applied to monitoring device 100 at measurement location 804. Additionally or alternatively, the conductive fibers may be used as a second electrical contact sensor (e.g., 803 in fig. 8D) that enables the user (subject) to create a closed electrical path to the heart when the subject touches the band 907 with the conductive fibers.

Fig. 9F depicts the monitoring device disposed on the forehead of the subject. In the illustrated embodiment, steps one through three of FIG. 9D have been performed. The monitoring device 100 is attached to a measurement location 804 (e.g. forehead), wherein the light source 113 and the photo detector 114 of the light sensor (and the temperature sensor 801 and/or the first electrical contact sensor 802 if comprised in the monitoring device 100) are placed on the measurement location 804 and in contact with the measurement location 804.

In some examples, using the forehead as the measurement location 804 may result in improved measurements for SpO2, PR, and PI. The perfused tissue in the forehead region is a few millimeters thick and is suitable for a reflection pulse oximeter, which has a small spacing (gap) between one or more light sources and one or more photodetectors (e.g., light source 113 and photodetector 114 in fig. 8A). In some embodiments, the monitoring device 100 has a spacing gap between 2.5mm and 7 mm. However, depending on the light sensor design, a separation gap of less than 2.5mm may be achieved. The monitoring device 100 can detect shallow perfused blood tissue from the forehead, provide waveforms with higher signal-to-noise ratios, and enable accurate detection of SpO2, PR, and PI even at very low perfusion levels.

The forehead may also be advantageous as a measurement site 804 during physical activity. Considering that the measurement location 804 is located on the head of the subject, it is not susceptible to motion artifacts. As a mechanism of self-protection against brain injury, the motor effects of the body are always minimized at the head of the subject through motor dumps effected by the bones and muscles. Furthermore, the forehead experiences shorter transmission delays for physiological changes (i.e., SpO2, PR, and/or PI changes, etc.) given its proximity to the brain, and also shows less vasoconstrictive response to cold. Optionally, a headband 908 (see fig. 9C) may be used to protect the monitoring device 100 from various events, such as mechanical shock, displacement, and direct sunlight exposure. The headband 908 may also improve optical, thermal, and/or electrical coupling with the measurement location 804 (e.g., forehead).

However, in embodiments where the monitoring device 100 includes first and second electrical contact sensors (e.g., 802, 803 in fig. 8D), the headband 908 may cover the second electrical contact sensor (e.g., 803 in fig. 8D). Accordingly, conductive fibers (not shown) may also be included in the headband 908 to create a fabric or headband having a conductive shape. The conductive fibers in the headband 908 create an electrical contact between the second electrical contact sensor and the headband 908. Whenever the subject touches the headband 908 with conductive fibers with a body part (i.e., fingertip 805 in fig. 8D), a closed electrical path (e.g., 806 in fig. 8D) to the subject's heart is created.

To enable the monitoring device 100 to wirelessly communicate with a host device (e.g., 105 in fig. 1), the conductive fibers in the headband 908 may be arranged so as not to block wave frequencies in the usable wireless frequency range. In another embodiment, a headband 908 with conductive fibers may be made part of the monitoring device 100 to perform: a function of raising the gain of the wireless antenna in the monitoring apparatus 100 by the conductive fiber; and/or improve optical, electrical, and thermal coupling (contact) with the measurement location 804 (e.g., forehead) when pressure is applied to the monitoring device 100 at the measurement location 804. Additionally or alternatively, the conductive fibers may be used as a second electrical contact sensor (e.g., 803 in fig. 8D) that enables the user (subject) to create a closed electrical path to the heart whenever the subject touches the headband 908 with conductive fibers using a body part (i.e., fingertip 805 in fig. 8D).

Fig. 9G depicts the monitoring device 100 being placed on the forehead of a subject with the light source 113 and the photodetector 114 of the light sensor (and the temperature sensor 801 and/or the first electrical contact sensor 802 if included in the monitoring device 100) being placed on and in contact with the measurement location 804. In this embodiment, adhesive bandage 913 is used to protect monitoring device 100 from various events, including mechanical shock, displacement, and direct sunlight exposure. The adhesive bandage 913 may also improve optical, thermal, and/or electrical coupling with the measurement location 804 (e.g., forehead).

However, in embodiments where the monitoring device 100 includes a first electrical contact sensor and a second electrical contact sensor (e.g., 802, 803 in fig. 8D), the adhesive bandage 913 may cover the second electrical contact sensor (e.g., 803 in fig. 8D). Thus, conductive fibers (not shown) may also be included in the adhesive bandage 913 to produce a bandage with conductive properties. The conductive fibers in the bandage 913 create an electrical contact between the second electrical contact sensor (e.g., 803 in fig. 8D) and the bandage 913. Whenever the subject touches the bandage 913 with a body part (i.e., fingertip 805 in fig. 8D) with conductive fibers, a closed electrical path (e.g., 806 in fig. 8D) to the subject's heart is created.

To enable the monitoring device 100 to wirelessly communicate with a host device (e.g., 105 in fig. 1), the conductive fibers in the bandage 913 may be arranged so as not to block wave frequencies in the usable wireless frequency range. In another embodiment, a bandage 913 having conductive fibers may be made part of the monitoring device 100, performing: a function of raising the gain of the wireless antenna in the monitoring apparatus 100 by the conductive fiber; and/or improve optical, electrical, and thermal coupling (contact) with the measurement location 804 (e.g., forehead) when pressure is applied to the monitoring device 100 at the measurement location 804. Additionally or alternatively, conductive fibers may be used as a second electrical contact sensor (e.g., 803 in fig. 8D) that enable the user (subject) to create a closed electrical path to the heart whenever the subject touches the headband 908 with conductive fibers using a body part (i.e., fingertip 805 in fig. 8D).

Fig. 9H depicts the monitoring device 100 placed on the forehead of the subject. The monitoring device 100 is attached to a measurement location 804 (e.g. forehead), wherein the light source 113 and the photo detector 114 of the light sensor (and the temperature sensor 801 and/or the first electrical contact sensor 802 if comprised in the monitoring device 100) are placed on the measurement location 804 and in contact with the measurement location 804. In this embodiment, a hat 914 may be used to protect the monitoring device 100 from various events, including mechanical shock, displacement, and direct sunlight exposure. The hat 914 may also improve optical, thermal, and/or electrical coupling with the measurement location 804 (e.g., forehead).

However, in embodiments where the monitoring device 100 includes a first electrical contact sensor and a second electrical contact sensor (e.g., 802, 803 in fig. 8D), the cap 914 may cover the second electrical contact sensor (e.g., 803 in fig. 8D). Accordingly, conductive fibers (not shown) may also be included in the cap 914 to create a fabric or cap 914 with conductive properties. The conductive fibers in the cap 914 create an electrical contact between the second electrical contact sensor and the cap 914. Whenever the subject touches the hat 914 with conductive fibers with a body part (i.e., the fingertip 805 in fig. 8D), a closed electrical path (e.g., 806 in fig. 8D) to the subject's heart is created.

To enable the monitoring device 100 to wirelessly communicate with a host device (e.g., 105 in fig. 1), the conductive fibers in the hat 914 may be arranged to not block wave frequencies in the usable wireless frequency range. In another embodiment, a cap 914 with conductive fibers may be made part of the monitoring device 100 to perform: a function of raising the gain of the wireless antenna in the monitoring apparatus 100 by the conductive fiber; and/or improve optical, electrical, and thermal coupling (contact) with the measurement location 804 (e.g., forehead) when pressure is applied to the monitoring device 100 at the measurement location 804. Additionally or alternatively, conductive fibers may be used as a second electrical contact sensor (e.g., 803 in fig. 8D) that enable the user (subject) to create a closed electrical path to the heart whenever the subject touches the hat 914 with conductive fibers with a body part (i.e., fingertip 805 in fig. 8D).

Fig. 9I depicts monitoring device 100 placed on the posterior pinna (ear) of a subject. The monitoring device 100 is attached to a measurement location 804 (e.g. an ear), wherein the light source 113 and the photo detector 114 of the light sensor (and the temperature sensor 801 and/or the first electrical contact sensor 802 if comprised in the monitoring device 100) are placed on the measurement location 804 and in contact with the measurement location 804. In some examples, the light emitted by the one or more light sources 113 may be visible through cartilage of the ear depending on the intensity of the light and the skin color of the user (see region 950).

In one aspect, using the posterior pinna as the measurement location 804 may result in improved measurements for SpO2, PR, and PI. The thickness of the ear cartilage in this region of the ear is typically only a few millimeters thick. Thus, the measurement location 804 is adapted for a reflection pulse oximeter with a small spacing (gap) between the light source(s) of the light sensor and the photodetector(s) (e.g., 113, 114 in fig. 8D).

The measurement location 804 enables the monitoring device 100 to detect shallow perfused blood tissue from the posterior auricle, provides a waveform with a signal-to-noise ratio, and enables accurate detection of SpO2, PR, and PI even at very low perfusion levels. The posterior auricle is also advantageous during physical activity as the measurement site 804. Considering that the measurement location 804 (e.g., ear) is located on the head of the subject, it is less susceptible to motion artifacts. As a mechanism of self-protection against brain injury, the motor effects of the body are always minimized at the head of the subject through motor dumps effected by the bones and muscles. Furthermore, the posterior auricle experiences shorter transit delays for physiological changes (i.e., SpO2, PR, and/or PI changes, etc.) and also shows less vasoconstrictive response to cold, given its proximity to the brain.

In some embodiments, adhesive tape 915 may be used to protect monitoring device 100 from various events, including mechanical shock, displacement, and direct sunlight exposure. The adhesive tape 915 may also improve optical, thermal, and/or electrical coupling with the measurement location 804 (posterior pinna). However, in embodiments where the monitoring device 100 includes a first electrical contact sensor and a second electrical contact sensor (e.g., 802, 803 in fig. 8D), the adhesive tape 915 may cover the second electrical contact sensor (e.g., 803 in fig. 8D). Accordingly, conductive fibers (not shown) may also be included in the adhesive tape 915 to produce an adhesive tape 915 having conductive properties. The conductive fibers in the adhesive tape 915 create an electrical contact between the second electrical contact sensor and the adhesive tape 915. Whenever the subject touches the adhesive tape 915 with conductive fibers with a body part (i.e., fingertip 805 in fig. 8D), a closed electrical path is created to the subject's heart (e.g., 806 in fig. 8D).

To enable the monitoring device 100 to wirelessly communicate with a host device (e.g., 105 in fig. 1), the conductive fibers in the adhesive tape 915 may be arranged so as not to block wave frequencies in the usable wireless frequency range. In another embodiment, an adhesive tape 915 with conductive fibers may be made part of the monitoring device 100, performing: a function of raising the gain of the wireless antenna in the monitoring apparatus 100 by the conductive fiber; and/or improve optical, electrical, and thermal coupling (contact) with measurement location 804 (e.g., the ear) when pressure is applied to monitoring device 100 at measurement location 804. Additionally or alternatively, conductive fibers may be used as a second electrical contact sensor (e.g., 803 in fig. 8D) that enable the user (subject) to create a closed electrical path to the heart whenever the subject touches the adhesive tape 915 with conductive fibers using a body part (i.e., fingertip 805 in fig. 8D).

9J-9P illustrate the steps of starting an application in a host device, connecting a monitoring device to the host device, and starting an exchange of data between the monitoring device and the host device to produce measurements and waveforms. As shown in fig. 9J and 9K, the wireless communication device in the host device (e.g., 116 in fig. 1) is implemented via settings 954 in a user settings interface 952 on the host device. For example, in one embodiment, the bluetooth wireless communication device is implemented using a switch 955 (see fig. 9K).

Next, as shown in fig. 9L, the user launches an application that interacts with the monitoring device, which may optionally cause a launch screen 956 to be displayed that has information related to the application (e.g., information such as the application name, the manufacturer's name, copyright, etc.). A screen 958 having selectable elements 960 is displayed (fig. 9M). In fig. 9M, selectable element 960 is labeled as a button for "scan," but other embodiments are not limited to such an implementation. When the user launches the application for the first time (e.g., the application has not been used before), in some embodiments, the application is locked to screen 958 until the user scans a valid barcode from a product indicium (e.g., product indicium 906 in fig. 9B).

When the user selects a selectable element 960 (e.g., a scan button), an optional notification 962 can be presented illustrating that the application is requesting access to an imaging device (e.g., a camera) on the host device (FIG. 9M). Imaging devices are used to scan barcodes on product indicia. Fig. 9N shows an example screen 964 displaying an image 966 of a scanned product indicia. In some embodiments, a pointer (e.g., a colored square; not shown) is generated on the screen of the host device around the image 966 of the scanned barcode. Additionally or alternatively, a sound such as a beep is generated to indicate that the bar code has been scanned and is valid. If the scanned bar code is invalid, a different indicator or indicators having different colors (e.g., red squares) are displayed adjacent to or around the invalid bar code. In some examples, a different sound (e.g., a chime) is generated in addition to or instead of the indicator. In other embodiments, the host device generates tactile feedback (e.g., vibrations generated by a tactile transducer) to indicate that the barcode is valid.

The application may display a measurement screen 968 (or cause it to be displayed) that includes a notification 970 that the monitoring device is not connected to the application (fig. 9P). The measurement screen 968 may be presented in a portrait (portrait) mode (see fig. 9P) or in a landscape (landscapes) mode. As shown in fig. 9P, a waveform diagram 920 (photoplethysmogram) is displayed in the measurement screen 968.

In fig. 9Q, SpO2916, PR 917, and PI 918 gauges, a body core temperature 919 gauge, and a waveform diagram 920 (photoplethysmogram) are displayed in a measurement screen 968. When the monitoring device includes a first electrical contact sensor and a second electrical contact sensor (e.g., 802, 803 in fig. 8D), and the user touches the second electrical contact sensor with a body part (e.g., a finger), a spot-check ECG waveform 921 is displayed in the measurement screen 968. The sampled ECG waveform 921 can be updated in real-time on the measurement screen 968 until the body part (e.g., finger) is not in contact with the second electrical contact sensor. When the user removes the body part from the second electrical contact sensor, the ECG waveform 921 can freeze on the measurement screen 968 and/or can disappear from the measurement screen 968. In some embodiments, the user may set settings for the ECG waveform 921 to remain frozen or to be removed.

Fig. 9R depicts a measurement screen 968 that displays SpO 2922 trends, PR 923 trends, and PI 924 trends in addition to SpO2916, PR 917, and PI 918 gauges, body core temperature 919 gauges, waveform 920, and ECG waveform 921. In other embodiments, fewer gauges and/or waveforms may be presented in the measurement screen 968. In some embodiments, the user can select which of the SpO 2922 trend, the PR 923 trend, and the PI 924 trend are to be displayed on the measurement screen 968. For example, a user may touch a particular meter on the measurement screen 968 to display a corresponding trend.

Fig. 9S depicts an example embodiment of a wearable hat that includes a monitoring device. Wearable cap 972 includes: built-in hat circuitry 927 that turns hat 972 into a wearable device for medical, fitness, and/or health applications. The cap 972 may also include built-in electroencephalogram (EEG) electrodes 928. Depending on the application, EEG electrodes 928 may be part of the fabric of cap 972 or attached to cap 972. Depending on the application, other light sensors, temperature sensors, electrical contact sensors, capacitive contact sensors, ultrasonic sensors, etc. may be part of the cap circuitry 927 or may be distributed around the cap layout (as EEG electrodes) and/or a portion of the fabric of the cap.

Hat circuitry 927 may or may not include some or all of the circuitry in a monitoring device (e.g., monitoring device 100 in fig. 1). The cap circuitry 927 senses one or more physiological measurements and/or environmental data, processes and conditions the physiological and/or environmental data, and wirelessly transmits the data to a host device (e.g., 105 in fig. 1). In one embodiment, the data is transmitted to the host device in real-time. For example, signals from the photodetector, EEG electrodes and/or temperature sensor are communicated to a host device.

The host device processes and analyzes the data, displays one or more measurements and trends, generates alerts or warnings, stores the data, shares the data, and the like. In one embodiment, cap circuitry 927 has the following functions:

1. sensing circuitry — hat circuitry 927 includes sensing circuitry 973, e.g., electronics and sensors as separate and/or distributed components, standard integrated circuitry, and/or Application Specific Integrated Circuits (ASICs), for sensing body temperature, ambient pressure, ambient ultraviolet light (a-band, B-band, and/or C-band), body hydration, non-invasive blood total hemoglobin, hydroxyhemoglobin, and methemoglobin, SpO2, PR, PI, plethysmography, non-invasive blood glucose levels, respiration rate, user's active calories and calories expended, EEG, etc., and sampled measurements of ECG waveforms, either continuously or at selected times.

2. Processing device, such as a low power processing device (e.g., ARM-based, ASIC, etc.) 974 receives data from sensing circuitry 973, processes the data, and wirelessly transmits the data to a host device (e.g., 105 in fig. 1) using communication device 977. In some embodiments, a processing device, such as ASIC circuitry, performs specialized signal processing functions to reduce the overall power consumption and footprint of hat circuitry 927. In a non-limiting example, processing device 974 performs functions with low complexity and low latency (i.e., hard real-time processing), and the host device processes data with higher complexity and latency (i.e., soft real-time processing). Depending on the application, processing device 974 may be part of hat circuitry 927 or may be distributed around a portion of the hat layout and/or the fabric of the hat.

3. Power management circuitry-Power management circuitry 975 includes a battery and manages the voltage and load of the battery, battery uptime, and charging. In one embodiment, the batteries in the cap 972 are charged by the wireless charger 926. The wireless charger 926 minimizes the circuitry isolation requirements and eliminates the need for cables and/or connectors. It is also contemplated that the user may place the cap on or near the wireless charger 926 in order to charge the batteries in the cap 972, which may also be convenient for the user. The voltage may be regulated by a boost converter and a buck converter, which may be designed to: a voltage level is provided that has a maximum noise level that matches the requirements of the battery. Depending on the application, the power management unit may be part of the hat circuitry 927, or may be distributed around a portion of the hat layout and/or the fabric of the hat.

4. Energy harvesting circuitry-energy harvesting circuitry 976 may be comprised of: pyroelectric, piezoelectric, pyroelectric, photovoltaic, ambient radiation transducers and electronic devices that convert some or all forms of energy in the environment into electricity that can directly power the hat circuitry 927 or can be used to recharge batteries in the power management circuitry 975 to recharge or increase battery uptime. Depending on the application, the transducers and electronics may be part of the hat circuitry 927 or may be distributed around a portion of the hat layout and/or the fabric of the hat.

5. Communication device — any suitable wireless communication device 977 may be used. In one embodiment, communication device 977 is a wireless broadcast unit for receiving and transmitting data to a host device (e.g., 105 in fig. 1) or a network (e.g., 120 in fig. 1). In a non-limiting example, the hat may communicate wirelessly using a router using IPV6 (internet protocol version 6) over bluetooth smart protocol. In one embodiment, communication device 977 supports multiple low power consumption protocols, such as bluetooth low energy, ZIGBEE, ANT, or some custom/proprietary low power consumption protocol. Depending on the application, the communication device 977 including one or more antennas may be part of the hat circuitry 927 or may be distributed around a portion of the hat layout and/or the fabric of the hat. In particular, the antenna design of low power radio may greatly benefit from the layout, materials and area of the hat, thereby enabling a distributed antenna layout with very high internal gain in the frequency band of interest, thereby significantly reducing the power consumption of the radio.

Fig. 9T illustrates several possible configurations for the wearable hat shown in fig. 9S. The configuration 930 includes a hat 972 and a monitoring device 100. Configuration 930 can be used on single-use disposable applications where cap 972 and cap circuitry 927 are discarded after the battery is empty. In this configuration 930, the battery is not rechargeable, and therefore does not require power management circuitry 975 or energy harvesting circuitry 976. In one embodiment, a non-rechargeable battery may be part of the monitoring device 100.

The arrangement 931 includes a cap 972, a monitoring device 100, and a removable battery 978. Configuration 931 can be used on a multi-use disposable application where cap 972 and cap circuitry 927 are used multiple times. In this embodiment, the battery 978 is replaced with a new battery whenever the charge on the battery 978 is low or empty. Battery 978 may consist of a standard non-rechargeable battery or may be a custom module with tamper-resistant memory that prevents the user from using batteries that are not provided by the manufacturer of cap 972.

Configuration 932 is similar to configuration 931, with the addition of energy harvesting circuitry 976. The energy harvesting circuitry 976 may be used to: increasing the amount of power on the removable battery 978, and extending the amount of time the removable battery 978 is used before replacement or recharging.

Configuration 933 includes hat 926, monitoring device 100, and a charger (e.g., wireless charger 926). Configuration 933 is used in reusable applications where the battery is rechargeable and a charger (e.g., wireless charger 926) is used to charge the battery. In one embodiment, the rechargeable battery circuitry, fuel gauge circuitry, protection circuitry, and wireless charging receiver circuitry may be part of the monitoring device 100.

Fig. 9U depicts an example embodiment of a wearable patch 940. The patch 940 includes: a monitoring device 100; and patch circuitry 941 that senses one or more physiological and/or environmental data, processes and conditions the physiological and/or environmental data, and wirelessly transmits the data to a host device (e.g., 105 in fig. 1). In one embodiment, the data is transmitted to the host device in real-time.

The host device processes and analyzes the data, displays one or more measurements and/or trends, generates one or more alerts or alarms, stores the data, and/or shares the data with another computing device. In one embodiment, patch circuitry 941 has similar functionality to cap circuitry 927 shown in fig. 9S. However, given the smaller size of the patch (when compared to a hat), the patch circuitry 941 is integrated and lightweight.

The patch 940 may be a disposable single-use patch, a multiple-use patch, or a reusable patch. A single fiber (monofilame) 942 may be used in place of a headband or adhesive tape to attach the patch to a measurement location (e.g., the forehead). In one embodiment, the filaments 942 are transparent or translucent filaments. The single fiber 942 may be attached to a housing 943 having a cavity in which the patch 940 is mounted. The housing 943 may be made to match the skin color of the subject to create a discreet monitoring solution working on a measurement site (e.g., the subject's forehead). The housing 943 with the single fiber 942 may make the monitoring solution discreet and improve the optical, electrical, and/or thermal coupling between the patch 940 and the measurement location. The single fiber 942 may protect the patch 940 from various events, such as mechanical shock, displacement, and direct sunlight exposure.

In reusable embodiments, the battery in the patch 940 may be a rechargeable battery that is charged by a charger (e.g., the wireless charger 926). The wireless charger 926 may reduce or minimize circuitry isolation requirements and reduce or eliminate the need for cables and/or connectors. Wireless charging may also be convenient to the user in view of the user placing the patch 940 on the wireless charger 926 or near the wireless charger 926 in order to charge the patch's battery. The monitoring solution with patch 940, housing 943 and single fiber 942 may be applied to measurement sites other than the forehead. For example, measurement locations such as arms, legs, wrists, feet, chest, and neck may be used. Further, in some applications, a reusable monitoring solution with patch 940, housing 943 and single fiber 942 may be integrated into a single device to reduce cost and simplify the usage workflow to make it more convenient for everyday use.

10A-10B depict a workflow for a host device to share data received from a monitoring device. Fig. 10 shows a screen 1000 displayed by an application on a host device (e.g., host device 105 in fig. 1). In one embodiment, an application may share a report 1001, a trend 1002, and a waveform 1003 with another computing device based on the selection of selectable element 1023. In fig. 10A, the selectable element 1023 is a button labeled "share," but other embodiments are not limited to such an implementation.

The report 1001 may be a combination of analytics and charts shared as files (i.e., PDF, PS, HTML, etc.) or designed to interact with the user. The trend 1002 may be a spreadsheet or database (interactive or as a file share) with data trend measurements over time. The waveform 1003 may be: a database or spreadsheet file that records the acquired waveforms, or raw data from which the waveforms and other variables of interest can be extracted.

In FIG. 10B, screen 1024 depicts an example data sharing method, e.g., transfer, Wi-Fi, message, mail, cloud, print, etc. One example of a method of transferring data sharing is AIRDROP by Apple. The available data sharing methods may vary depending on the configuration of the host device 105 and/or the type of data being shared.

In one embodiment, the user may share the report after the monitoring device is connected to the host device and the host device is displaying one or more meters, one or more waveforms, and/or one or more trends (e.g., SpO2, PR, PI, and temperature measurements, photoplethysmography, and/or ECG waveforms) received over a given period of time. Fig. 10C-10L illustrate example reports with analysis based on data collected by a user wearing the monitoring device 100 over a given period of time (e.g., overnight or 569 minutes). The example report includes a particular value, and other embodiments may present different values. An example report is:

1. SpO2 measurements over time-report 1004 in FIG. 10C shows an example of SpO2 trends. The report 1004 may be created by an application on the host device. Normal SpO2 levels are typically between 94% and 100%. However, during conditions of sleep, exercise, or high pressure or in a high altitude location, the SpO2 reading reaches a lower value (i.e., less than 94%). The example graph 1004 includes: optional footnote 1025, with information about the data that has been collected (i.e., identification 1026 of the monitoring device used in the data collection, the period 1027 of the data collection, and the date and time 1027 the report was created). Although not shown in fig. 10D-10L, the embodiment shown in fig. 10D-10L may also include footnotes.

2. PR measurements over time-report 1005 in fig. 10D shows an example of a PR trend. Report 1005 may be created by an application on a host device. The normal resting PR for an adult is typically in the range of from 60bpm to 100 bpm. However, PR readings may reach higher values during sleep, exercise or high stress situations or in high altitude positions. Reports having different values may be generated in other embodiments.

3. PI measurements over time-report 1006 in fig. 10E shows an example of a PI trend. Example report 1006 may be created by an application on the host device. The PI value is typically from 0.02% (very weak ripple signal) to 20% (very strong ripple signal). The limit PI value may indicate an uncomfortable condition (e.g., cold, hot, pressure, etc.). Reports having different values may be generated in other embodiments.

4. SpO2 distribution-report 1007 in FIG. 10F shows an example of SpO2 distribution. The example report 1007 may be created by an application on the host device. As shown in example report 1007, SpO2 values were within 94% and 100% for 87.4% of the time (i.e., 497 minutes), and SpO2 values were between 88% and 93% for 12% of the time (i.e., 68 minutes). Reports having different values may be generated in other embodiments.

5. PR Profile-report 1008 in FIG. 10G shows an example of a PR profile. The example report 1008 may be created by an application on the host device. As shown in example report 1008, PR values were within 60bpm and 80bpm for 58.1% of the time (i.e., 331 minutes), and between 50bpm and 59bpm for 39.1% of the time (i.e., 222 minutes). Reports having different values may be generated in other embodiments.

6. PI distribution-report 1009 in fig. 10H shows an example of PI distribution. Example report 1009 may be created by an application on the host device. As shown in the example graph, the PI value is within 0.1% and 0.5% for 64.2% of the time (i.e., 365 minutes), and between 0.01% and 0.1% for 35% of the time (i.e., 199 minutes). Reports having different values may be generated in other embodiments.

7. Hourly SpO2 desaturation — report 1010 in FIG. 10I shows the number of desaturations per hour based on baseline. The example report 1010 may be created by an application on a host device. According to the example graph 1010, the user has a desaturation of greater than 4% in magnitude 1.9 times per hour. Healthy people will typically have desaturations of greater than 4% with an amplitude of less than about 5 times per hour. These values may increase during training or in high stress situations or in high altitude locations. Reports having different values may be generated in other embodiments.

8. Percentage of cumulative time SpO2 was less than the threshold-report 1011 in FIG. 10J shows percentage of cumulative time SpO2 was less than the threshold. Report 1011 may be created by an application on a host device. According to example plot 1011, the user's SpO2 value is less than 90% over a period of 1.5% (i.e., 8.5 minutes). Healthy people will typically remain at a very small percentage of SpO2 less than 90% of the time. This percentage of time may increase during training or in high stress situations or in high altitude locations. Reports having different values may be generated in other embodiments.

9. PR fluctuation profile-PR fluctuation rate represents the short-term variability of heart rate. In one embodiment, the PR fluctuation rate is calculated following the steps described in the embodiment depicted in fig. 10P, where the measurement data in block 1017 is a sequence of instantaneous and sequential measurements of PR and the corresponding distribution is one of the statistics calculated in block 1020. Generally, higher values of PR fluctuation are better for healthy adults. An increasing trend in PR fluctuation rate is positive and indicates positive adaptation and/or improvement in fitness. Report 1012 in fig. 10K shows an example of PR fluctuation distribution. Report 1012 may be created by an application on the host device. As shown in the example graph, the PR fluctuation values were between 0.46bpm and 1bpm over more than 50% of the time (i.e., 286 minutes). Reports having different values may be generated in other embodiments.

10. PI log fluctuation distribution-PI log fluctuation rate indicates short-term variability in perfusion. In one embodiment, the PI log-fluctuation rate is calculated following the steps described in the embodiment depicted in fig. 10O, where the measurement data in block 1016 is a sequence of instantaneous and sequential measurements of the PI, and the corresponding distribution is one of the statistics calculated in block 1020. Generally, lower values of PI log fluctuation are better for healthy adults. The reduced PI log-fluctuation rate trend is positive and indicates positive adaptation and/or a reduction in the overall stress level. Report 1013 in fig. 10L shows an example of PI log fluctuation distribution. The report 1013 may be created by an application on the host device. As shown in the example graph, the PI log fluctuation values were within 9.4% and 15% over a time period exceeding 52% (i.e., 286 minutes). Reports having different values may be generated in other embodiments.

Additionally or alternatively, the user may share trends for SpO2, PR, PI, and temperature measurements in the file at any time through an application on the host device (e.g., trend 1002 in fig. 10A). One example of a file is a Comma Separated Values (CSV) file. Files can be opened and displayed directly by the host device, or by most rendering or spreadsheet software such as EXCEL and NUMBERS. In FIG. 10M, an example CSV file 1014 contains 6 columns. Other embodiments may display different values, different numbers of rows, and/or different numbers of columns. The columns in the example file 1014 are:

1. date/time-date and time at which each measurement taken was stored. Measurements may be stored once per second (for 12 hour trend storage), once every 2 seconds (for 24 hour trend storage), once every 3 seconds (for 36 hour trend storage), and once every 4 seconds (for 48 hour trend storage). Other embodiments may store the measurement results at different times.

2. The product is marked with a bar code. An 8-bit hexadecimal number identifying the monitoring device used at the corresponding time and date.

3. SpO2 (%) -SpO 2 measurement.

4. PR (bpm) -PR measurement.

5. PI (%) -PI measurement.

6. Temperature (. degree. C.) -core body temperature measurement.

In some examples, a user may share a waveform (e.g., waveform 1003 in fig. 10A) through the application on the host device. In one embodiment, the waveforms are shared in the form of raw data, auxiliary variables, and parameters (up to a certain number of hours) collected by the monitoring device, and stored in the host device. Other embodiments may share waveforms in different forms. The data may be stored to a database file (e.g., waveformmsdb.db) using an encoded data format. The data may be used for in-depth technical analysis of potential problems detected by the user, or for offline calculation of other parameters of interest (e.g., heart rate change rate, respiration rate, etc.), or for offline recalculation of values of SpO2, PR, PI, temperature, photoplethysmogram, and ECG waveforms using a customized algorithm that may be better suited to a particular offline application.

FIG. 10N depicts an example format of file 1015. File 1015 includes three columns: index, timestamp, and data. The index column indexes the rows of file 1015. The timestamp column has an example format YYYY-MM-DD hh: mm: uuu, wherein YYYY-MM-DD is year, month and day, hh: mm: uuu is the time in hours, minutes, seconds, and milliseconds, and is the date and time when the measurement data is saved to file 1015. Because the measurement data is saved to the file 1015 by the host device in real-time, each timestamp value is an approximation of the time at which the measurement data was actually collected. The timestamp value may be used to synchronize the measurement data stored in the file 1015 with other measurement systems in a timely manner to perform analysis using such time synchronization. The data column is the actual data saved to the file 1015.

In fig. 10N, the data column has three arrays of example data separated by square brackets ([ first data array ] [ second data array ] [ third data array ]. each row of the three arrays has forty samples. the samples of each data array are collected synchronously by the monitoring device every twenty milliseconds (50Hz sampling frequency), transmitted wirelessly to the host device where the data is stored, hi one embodiment, the host device stores the data in real time to a temporary buffer (to reduce latency requirements) and then saves the data in the buffer to a file 1015 every N seconds, where N is an integer or fraction greater than zero. Such as, for example, battery voltage, SoC temperature, current settings for one or more light sources of the light sensor, gains of the light front end, temperature front end, and ECG front end, SoC usage time counter, SoC standby timer counter, ambient light intensity detected by the light front end, device identification number, and the like.

In some embodiments, to increase the battery uptime of the host device (e.g., a smart watch or smart phone), a waveform (e.g., waveform 1003 in fig. 10A) may not be stored on the host device. In this case, the user may disable the reservoir. FIG. 12B illustrates an example embodiment of a screen to set up an application on a host device, where a user may enable or disable 1206 the storage of the last twelve hours of waveform data.

There are situations where it is desirable to take screenshots of meter and trend data, for logging by the user, or for sharing with third parties. Typically, the host device provides this functionality as a standard feature. For example, in the case of an iOS device from Apple inc, the user presses and holds one of the top (or side) button and the volume button at the same time to capture a screenshot of the display. The measurement screenshot may be displayed on a display of the iOS device and/or shared by the iOS device.

FIG. 10O illustrates an example flow chart of a method of calculating log-fluctuation rate of measurement data. In the process shown in FIG. 10O, for example, the PI log fluctuation distribution depicted in FIG. 10L may be generated. The method depicted in FIG. 10P, for example, may produce the PR fluctuation profile depicted in FIG. 10K. In fig. 10O, the measurement data of the measurement data stream is normalized. In a non-limiting example, the measurement data is normalized by determining the natural logarithm of the measurement data (block 1016). For example, the measurement data stream may be a PI, PR, or SpO2 measurement data stream.

At block 1017, the normalized measurement data is filtered to produce a symmetric or asymmetric data stream (near the origin) that represents the normalized rate of change of the raw measurement data stream. In one embodiment, a band pass filter is used to filter the normalized measurement data. At block 1018, the absolute value of the measurement data is determined to change all rate of change values to positive values. The measurement data is filtered at block 1019 to obtain a log-wave-rate data stream. In one embodiment, the measurement data is filtered by a low pass filter for averaging purposes. The resulting measurement data represents the log-wave rate of the raw measurement data stream over time. Statistical analysis (block 1020) may be applied to the generated measurement data to determine, for example, a volatility metric and/or a probability distribution.

FIG. 10P depicts a flowchart of an example method of calculating a volatility of measurement data. Unlike the generated data generated by the method of FIG. 10O, the volatility measurements generated by the embodiment of FIG. 10P have the same units as the raw measurement data stream. The method of fig. 10P is the same process as the method of fig. 10O, except for block 1016, which is omitted in fig. 10P. Thus, the non-normalized measurement data is filtered at block 1021. In some embodiments, block 1018 and block 1019 may be omitted from the method in fig. 10P, and the statistical analysis (block 1020) is performed after block 1021.

In embodiments where blocks 1018 and 1019 are omitted, the data may have positive and negative values. The resulting statistics are then computed in block 1020, taking into account whether the inline probability distribution is symmetric or asymmetric around the origin. For example, in some embodiments, because block 1018 is not used, the mean may be zero, while second-order moments (e.g., variance, standard deviation) or higher-order moments may not. Thus, for embodiments in which blocks 1018 and 1019 are omitted, the second or higher order moments may more appropriately represent the potential fluctuation rate or rate of change of the data. Further, in some examples, the PR and PI log fluctuation distributions depicted in fig. 10K and 10L, respectively, may be replaced by reports depicting PR and PI log fluctuation rates over time.

FIG. 10Q illustrates a flow chart of an example method of calculating Ln log fluctuation rate of measurement data. The illustrated method is a generalization of the method illustrated in FIG. 10O. Accordingly, some of the blocks in fig. 10O are in the process shown in fig. 10Q, and are not described in further detail for the sake of brevity.

Initially, as indicated at block 1016, the measurement data of the measurement data stream is normalized. In a non-limiting example, the measurement data is normalized by determining the natural logarithm of the measurement data (block 1016). The normalized measurement data is filtered to produce a symmetric data stream (near the origin) that represents the normalized rate of change of the raw measurement data stream (block 1017). At block 1018, an absolute value of the measurement data is determined to change all rate of change values to positive values.

Next, as indicated at block 1030, the measurement data is raised to the nth power. The number n may be any positive integer. For n-1, the algorithm described in fig. 10Q becomes the same as the algorithm described in fig. 10O. The measurement data is then filtered at block 1019. Next, the nth root of the data is determined to provide a higher or lower weight for each measurement data sample based on the magnitude of the measurement data (block 1031). Block 1022 converts the measurement data back to the same scale as the measurement data of the raw measurement data stream. In one embodiment, blocks 1019 and 1030 are performed concurrently. Statistical analysis (block 1020) may be applied to the generated measurement data to determine, for example, a volatility metric and/or a probability distribution.

Fig. 10R depicts a flow chart of a method of calculating Ln fluctuation rate of the measurement data. The method of fig. 10R is the same process as the method of fig. 10Q, except for block 1016, which is omitted in fig. 10R. Accordingly, the non-normalized measurement data is filtered at block 1032. In some embodiments, the method of fig. 10R may be used to calculate the volatility of the data stream when the units of the calculated volatility values are the same as the measurement data (without normalization) and when higher or lower weighting is performed for each measurement data sample based on the magnitude of the measurement via block 1031.

FIG. 11A illustrates an example screen of identification parameters generated by an application on a host device. The example screen 1028 may include one or more of the following identification parameters:

1. serial number — when the monitoring device is connected to the host device, the host device may display an M-bit hexadecimal number in screen 1028, where M is a number greater than zero. For example, an M-bit hexadecimal number is a 16-bit hexadecimal number. In one embodiment, the M-bit hexadecimal number is the serial number of the monitoring device.

2. Barcode-when the monitoring device is connected to the host device, the host device may display in screen 1028: a P-bit hexadecimal number identifying the monitoring device, where P is a number greater than zero. For example, a P-bit hexadecimal number is a bar code from a product tag (e.g., product tag 906 in FIG. 9B). Further, the host device may display the barcode in the start screen of the application and/or in a trend file (e.g., data file 1015 in fig. 10N) shared by the host device. In a non-limiting example, the P-bit hexadecimal number is an 8-bit hexadecimal number.

3. Lot number — when the monitoring device is connected to the host device, the host device can display the manufacturing lot number of the monitoring device in screen 1028.

4. Validity period-when the monitoring device is connected to the host device, the host device may display in screen 1028: after which the monitoring device should not be sold or used.

5. Model — when the monitoring device is connected to the host device, the host device may display in screen 1028: the model number of the monitoring device is a Q-bit hexadecimal number. Q is a number greater than zero. For example, a Q-bit hexadecimal number is a 4-bit hexadecimal number.

6. Version-when a monitoring device is connected to a host device, the host device may display an R-bit hexadecimal number of the version number of the monitoring device. For example, an R-bit hexadecimal number is a 4-bit hexadecimal number.

7. Application version-when the monitoring device is connected to the host device, the host device can display the S-bit number of the software version. In a non-limiting example, the S-bit number is a three-bit number.

FIG. 11B depicts an example screen of hardware diagnostic parameters generated by an application. The example screen 1029 may include one or more of the following parameters:

1. LED power-LED power parameter display in screen 1029: the power level (reported at the timestamp) for each light source of the light sensor in the monitoring device. For example, the power levels of light source one (LED1) and light source two (LED2) are presented. The power level of each light source varies between 0 and 100%.

2. Electronic gain-the electronic gain parameter in screen 1029 represents the analog front end electronic gain (reported at the timestamp) of the monitoring device. The electronic gain varies between 0dB and 40 dB.

3. Ambient light — the ambient light parameter in screen 1029 represents the ambient light intensity (reported at a timestamp) detected by one or more photodetectors in the light sensors in the monitoring device. It varies between 0 and 100%.

4. SoC temperature-the SoC parameters in screen 1029 represent the System-on-chip (SoC) temperature in the monitoring device. The SoC temperature may be displayed in degrees celsius (reported at the timestamp) or in degrees fahrenheit.

5. Battery Voltage — the battery voltage parameter in screen 1029 represents the battery voltage in volts (reported at the timestamp) of the battery in the monitoring device.

6. Standby time — the standby time parameter in screen 1029 represents the amount of time (reported at the timestamp) that the ally connected device has been activated and disconnected from the host device.

7. Usage time — the usage time parameter in screen 1029 represents the amount of time (reported at a timestamp) of device 100 that has been activated and connected to host device 105.

8. Timestamp — the timestamp parameter in screen 1029 is the date and time when one or more hardware diagnostic parameters were reported.

Fig. 11C depicts an example embodiment relating to sharing measurement data with a technical support team of monitoring devices. The host device displays screen 1100 when the user wants to share measurement data in this example embodiment, the user shares identification and hardware diagnostic parameters and variables via email messages, but other embodiments are not limited to email. The user may also add comments about a particular question or a found question in portion 1101. Several other methods may be used to share such information with the manufacturer. Further, depending on the problem, the user may select a file (e.g., file 1015 in FIG. 10N) that shares waveform data with the technical support team for in-depth technical analysis of the potential problem detected by the user.

In some applications, systems including monitoring devices, host devices, and applications may include built-in alarm/warning systems to ensure that a user (e.g., a patient) is informed and/or safe. FIG. 12A depicts an example embodiment of an alarm/warning system. Fig. 12A shows an example screen 1200 in which the meter 1201 may flash a color (e.g., red) whenever a measurement value crosses an upper or lower preset limit, or whenever the monitoring device stops providing measurement data. Additionally or alternatively, an audible alarm/warning 1203 (e.g., voice, sound) is generated and/or a written message 1202 is displayed whenever the measured value crosses an upper or lower preset limit, or whenever the monitoring device stops providing measurement data.

Example alerts/warnings are now described. Example alarms are listed in order of decreasing priority, but other embodiments are not limited to the priority order shown.

1. The monitoring device is not connected to the host device — this alarm is raised whenever the monitoring device is not connected to the host device. The visual warning may include: causing one or more gauges to blink and/or be shown in phantom. The audible alert may include a beep and/or an audible message, e.g., "device not connected". In one embodiment, this alarm/alert has the highest priority given that the alert indicates that the monitoring device is not connected to a host device (which may be out of range, damaged, or turned off).

2. Battery depletion-this warning is issued whenever the battery in the monitoring device is low or depleted. The visual warning may include: causing a battery icon (e.g., battery icon 1204) to blink and/or be displayed in phantom. The audible alert may include a beep and/or an audible message, such as "battery is drained or low". In one embodiment, this alarm/alert has the second highest priority. In some embodiments, when the battery on the monitoring device is low, the meter is no longer displayed on the host device. When the battery on the monitoring device is drained, the measurement is not displayed on the host device.

3. The monitoring device is searching for a valid signal-this warning is issued whenever the monitoring device is connected to the host device but the measurement results are not yet displayed on the host device, either because the monitoring device is not correctly placed on the subject, or the collected data is within a transient time, where there is not enough collected data to produce a reliable set of measurement results. In one embodiment, this alarm/alert has the third highest priority. The visual warning may include causing one or more gauges to blink and/or be displayed in phantom. The audible alert may include a beep and/or an audible message, such as a "search signal.

4. The wireless connection to the host device is unreliable-this warning is issued whenever the monitoring device has an unreliable wireless connection to the host device. In one embodiment, the visual warning causes all of the gauges to flash a color, for example, red. The audible alert may include a beep and/or an audible message, such as "bad connection".

5. SpO2 measures that an upper or lower limit has been crossed-this alarm is raised whenever the SpO2 value is outside of the preset normal limits. In one embodiment, the visual warning causes the SpO2 meter to flash color (e.g., red) and/or to be displayed in a different color. The audible alert may include a beep and/or an audible message, such as a "saturation alert". In some examples, as will be described in more detail in connection with fig. 12B, the upper and/or lower limits of the SpO2 measurement may be defined by the user in the settings screen.

6. PR measurements have crossed an upper or lower limit-this alarm is raised whenever the PR value is outside of the preset normal limits. In one embodiment, the visual warning causes the PR meter to flash a color (e.g., red) and/or display in a different color. The audible alert may include a beep and/or an audible message, such as a "pulse rate alert". In some instances, as will be described in more detail in connection with fig. 12B, an upper limit and/or a lower limit of PR measurement results may be defined by a user in a setup screen.

7. SpO2 and PR measurements have crossed an upper or lower limit-this alarm is raised whenever both SpO2 and PR values are outside preset normal limits. In one embodiment, the visual warning causes the SpO2 and PR meter to flash color (e.g., red) and/or to be displayed in a different color. The audible alert may include a beep and/or an audible message, such as a "saturation and pulse rate alert".

8. Low battery in the host device-this warning is issued whenever the battery in the host device is low or exhausted. In one embodiment, the visual alert causes a message such as "charge host device" to be displayed. The audible alert may include a beep and/or an audible message, such as "charge the host device. In a non-limiting example, the warning is issued when the amount of charge on the battery in the host device is less than 23%, but other embodiments are not limited to this percentage value.

FIG. 12B depicts an example setup screen for an application. Screen 1205 enables the user to set alarm/warning limits and audio settings. A selectable control 1206 (e.g., a switch) enables/disables the audible alert. Selectable option 1207 enables the user to set a quiet time interval (i.e., 30 seconds, 60 seconds, 90 seconds, or 120 seconds). The silence interval is used to silence the alert for a given period of time and will be activated when the alarm/alert is activated and the user touches one of the gauges (i.e., SpO2, PR, PI, or temperature). The warning/alarm will be suspended during the quiet interval and then automatically resumed after the quiet interval expires. Optional elements 1208 and 1209 (e.g., sliding control elements) enable a user to set alarm limits for a particular meter. For example, in the illustrated embodiment, alarm limits for the SpO2 and PR meters may be set and selected values for each of the upper and lower limits displayed. In one embodiment, the SpO2 and PR alarm/warning limits are initially set to their default values.

In fig. 12B, selectable option 1210 (labeled voice gap) enables the user to define the periodicity of the voice-based measurements. In the example embodiment depicted in fig. 12B, the periodic option is every 30 seconds, 60 seconds, 120 seconds, or "never". For example, if the user selects the 30 second option, every 30 seconds the host device will speak to the user through the vocoder that it announces the current measurements. The host device, through its audio system, or through an audio system (i.e., headphones, speakers, vehicle sound system, etc.) that is wirelessly or wired to the host device, may speak to the user, e.g., telling "SpO 2 is one percent, PR is sixty-five hops per minute, PI is one percent, and the temperature is thirty-six degrees celsius. This functionality enables the user to turn off the screen of the host device or drive, exercise, etc. while wearing the monitoring device and having no direct access to the display screen on the host device, still be able to periodically hear their current measurements.

If the user does not want to hear the measurement, then the option of "never" disable the voice-based measurement in selectable options 1205. In some embodiments, the host device may be set to provide a voice-based measurement whenever one or some of the measurements change beyond a certain threshold (absolute or relative). For example, the host device may audibly output the current measurement whenever the value of SpO2 changes by more than +/-2 points, or the value of PR changes by more than +/-5bpm, or the value of PI changes by more than +/-10%, or the value of temperature changes by more than +/-0.3 degrees Celsius, based on the last measurement it was spoken of. The measurements taught may also be triggered based on how the rate of change of a particular measured variable (first derivative), or the rate of change of the rate of change (second derivative), etc., changes in time.

Additionally or alternatively, the user is informed of the measurement trend (or rate of change) over time since the last spoken measurement. For example, the host device may speak a message to the user, e.g., "SpO 2 is ninety-four percent, decreasing," PR is one hundred hops per minute, steady, "" PI is one-half-two percent, increasing, "" temperature is thirty-six degrees celsius, and steady. The rate of change may also be numerically specified, or qualitatively specified, e.g., "slowly increasing," "rapidly increasing," "slowly decreasing," or "rapidly decreasing," etc. The type and content of the measurement result message, the triggering rules, and the information contained therein depend on the monitoring application and its specific needs.

In one aspect, the host device 105 is restricted to applications (single applications) with hardware buttons, and access to the application menu is disabled and protected by a password (or some other form of authentication) to prevent unauthorized users from changing settings or disabling applications. In one example, when the host device is an iOS device, the application is compatible with an iOS-booted access mode. The boot access mode temporarily limits the iOS device to a single application and allows the user to control which features of the application are available. The default behavior of the application during the boot access portion may be:

1. application termination is disabled.

2. The application menu is disabled.

3. The portrait view and landscape view are enabled.

4. The hardware button is disabled (i.e., volume, sleep/wake, etc.).

5. The user can silence the audible warning (if enabled and activated) for a period of time by tapping on any meter. However, the audible warning will automatically continue after expiration of the silence duration (i.e., 30 seconds, 60 seconds, 90 seconds, or 120 seconds). The audible alert may be permanently disabled before the boot access portion is initiated, if desired.

The depicted alarm/alert system of fig. 12A-12B can also forward information related to active alarms/alerts to third parties via the host device. The host device may send the notification (wirelessly or wired) directly to the third party. Example third parties include, but are not limited to, nurses, doctors, and caregivers. Additionally or alternatively, the notification is sent to a central alarm/warning system, which in turn forwards the notification to the appropriate recipient.

In some embodiments, the battery is non-rechargeable. Information relating to the charge on the battery is provided to the user and/or device for use at selected times or continuously to enable the user to predict when the monitoring device is replaced. The battery icon 1204 shown in fig. 12A is shown in detail in fig. 13A. Fig. 13A depicts a battery icon and example battery states (e.g., full, not full, low, and depleted).

13B-13C illustrate a method for calculating the available time for a battery in a monitoring device. The method does not use specialized circuitry that may increase the manufacturing cost of the monitoring device. The challenge to accurately estimate battery age based directly on battery voltage is that battery voltage changes very little over a large portion of its discharge curve, making the mapping of battery voltage to battery age (or remaining time) unreliable.

Fig. 13B shows an example battery discharge curve. The cell voltage (x-axis) starts at Va (fully charged) and ends at Vc (fully discharged). From Va to Vb, the battery discharge curve 1300 is too vertical to produce an accurate estimate of the battery age (y-axis) based on the battery voltage. The y-axis may represent time, percentage, joules, or another suitable value. The challenge of estimating battery uptime is addressed by a hybrid approach to estimating battery uptime. The hybrid approach uses a combination of intelligent counters and battery voltage measurements to more accurately estimate the battery age.

An example method of estimating battery uptime of a battery in a monitoring device is shown in fig. 13C. The monitoring device is activated and a processing device (e.g., 102 in fig. 1) in the monitoring device begins to use the counter T (blocks 1310 and 1311). Ta represents the time to fully discharge the battery when the monitoring device is in operation. For example, if the battery usable time has a typical value, a minimum value, a maximum value, or an average value of 24 hours, Ta is set to a value corresponding to the battery usable time of 24 hours.

In one embodiment, the usage counter is implemented in non-volatile memory (e.g., flash memory) so that the counter value is not lost in the event of a power transient. At block 1312, a usage counter is periodically decremented by the processing device based on monitoring electrical loads in circuitry of the device. The higher the power consumption of a particular load, the greater the corresponding decrement (Δ Ti). Some loads are constant over time and some tend to vary according to known factors. For example, the required current of the light source (LED) may be varied depending on the light opacity of the measurement location. Therefore, the corresponding Δ Ti for a particular LED should be adjusted according to its set current. The larger the current, the larger the Δ Ti value.

At block 1313, when the battery voltage drops below Vb (fig. 13A), the amount of battery available time remaining (e.g., the amount of power on the battery) is estimated by a function f (Vbat, Tb). The function f (Vbat, Tb) can be implemented in several ways. One implementation for f (Vbat, Tb) is the minimum of two: a battery available time estimate from a battery voltage curve (as depicted in fig. 13B); and, based on the last battery available time estimate (Tb) using the counter, making f (Vbat, Tb) Tb equal to Vb with Vbat. This ensures functional continuity of the measurement result of the battery usable time even if the battery voltage momentarily increases due to changes in the ambient temperature, the load of the circuit system, and the like. At block 1314, when the battery voltage drops below Vc (fig. 13A), the battery is considered to be fully discharged (depleted). The different stages of battery age estimation shown in fig. 13A may be used to inform the user via battery icon 1204. The phase may also be used to trigger a battery depletion alarm/warning when the battery is fully discharged.

The usage counter implemented in block 1311 of fig. 13C may also be used to monitor the usage time of the monitoring device. In some embodiments, an additional counter (standby counter) may be used to account for the amount of time the monitoring device is in standby mode (i.e., activated but not connected to the host device). The standby counter may be implemented in the same non-volatile memory (e.g., flash memory) as the usage counter. Fig. 11B depicts the use time and standby time in screen 1029. The usage time and the standby time are calculated based on the aforementioned battery and standby counter implemented in the nonvolatile memory, and the values of the usage counter and the standby counter are wirelessly and periodically transmitted to the host device. In one embodiment, the use counter and the standby counter may be used to disable the monitoring device once the use and/or standby counter reaches a particular value, because the use counter and the standby counter may not be deleted from the outside, or by an unauthorized user, without deleting the entire firmware, thereby rendering the monitoring device inoperable. This prevents unauthorized users from replacing non-rechargeable batteries (tampering) in order to enable the monitoring device to be used for a longer period of time. The use counter and the standby counter may increase the safety of the monitoring device, since the monitoring device may not be repaired or disassembled due to incompatibility with circuitry in the monitoring device.

Another security feature may be implemented by: a voltage timer or counter ("voltage timer") is started each time the voltage of the battery falls below a certain threshold. If the battery voltage remains below the threshold until the voltage timer reaches a predefined value, then the battery voltage may not increase beyond the threshold as long as the battery is non-rechargeable, and a flag is set in non-volatile memory indicating that the battery is fully discharged. Thereafter, in the event that the battery voltage becomes greater than the threshold, and the counter expires (e.g., on use and/or standby), an inference may be made that an unauthorized user (or third party) has tampered with the monitoring device. In this case, the firmware of the monitoring device may reset the processing device (e.g., processing device 102 in fig. 1) and reach an idle state to prevent unauthorized use or reuse of the monitoring device.

FIG. 13D depicts a flow diagram of a tamper-resistant method for monitoring a device. Initially, as shown at block 1320, a determination is made as to whether the monitoring device is active and in use. If the monitoring device is in use, the process passes to block 1321, where the usage counter is incremented. A determination is made at block 1322 whether the usage counter has reached its maximum value. If its maximum value is reached, the method continues at block 1323, where the monitoring device stops operating and may not be used. In an alternative embodiment, if the usage counter reaches its maximum value, block 1323 may be performed only after the connection (e.g., wireless connection) between the monitoring device and the host device is lost to stop operation. This ensures that the patient or user is not put at risk due to lack of operation of the monitoring device because the usage counter has reached its limit (maximum).

Returning to block 1320, if the monitoring device is not in use, then it is assumed that the monitoring device is in a standby mode and the standby counter is incremented at block 1324. A determination is made at block 1325 whether the standby counter has reached its maximum value. If its maximum value is reached, the method passes to block 1323, where the monitoring device stops operating and may not be used. Further, in an alternative embodiment, if the standby counter reaches its maximum value, block 1323 may only be performed after the connection (e.g., wireless connection) between the monitoring device and the host device is lost to cease operation. This ensures that the patient or user is not put at risk due to lack of operation of the monitoring device because the standby counter has reached its limit (maximum).

When the standby counter and the usage counter have not reached the maximum value, the method continues at block 1326, where it is determined whether the battery voltage is below a predefined voltage threshold. If the maximum value is reached, the threshold counter is incremented at block 1327. Otherwise, the threshold counter is reset at block 1328.

At block 1329 it is determined whether the threshold counter has reached its maximum value. If its maximum value is reached, the method passes to block 1330, where the monitoring device stops operating (e.g., immediately), continues normal operation until a standby or usage counter expires, or stops operating after the battery voltage increases above a predefined voltage threshold. When the threshold counter has not reached its maximum value, the method returns to block 1320.

In some embodiments, the standby counter, the usage counter, and the threshold counter are implemented in a non-volatile memory of the monitoring device. Alternatively, the threshold counter may be implemented in volatile memory in the monitoring device, or in volatile memory of the host device, considering that threshold counters are typically used to measure time intervals that are much smaller than typical usage time intervals or standby time intervals. Implementing the counter in non-volatile memory reduces the chance of a power interruption, failure, or reset occurring that can cause the counter to lose its current value.

FIG. 14A depicts a first example method of determining an effective noise floor of a monitoring device. Initially, a monitoring device is turned on at block 1400. For example, in one embodiment, the monitoring device is turned on by removing the first tab and pressing an indicator (e.g., tab 909 and indicator 910 in fig. 9D). In block 1402, a monitoring device is connected (e.g., wirelessly connected) to a host device. In one embodiment, the monitoring device pairs with the host device using bluetooth.

Once the connection between the monitoring device and the host device is established, an effective noise floor measurement is performed on the monitoring device (block 1404). In a non-limiting example, the monitoring device is placed in a dark environment (e.g., limited ambient light or no ambient light) for effective noise floor measurement. In another non-limiting example, the monitoring device modulates the light source signal and/or demodulates the received light signal to enable effective noise floor measurement under ambient light interference and/or electromagnetic interference. The modulation algorithm and the demodulation algorithm prevent ambient light signals and/or electromagnetic interference from interfering with the effective noise floor measurement. The noise floor measurement is used to: the signal-to-noise ratio and the metric quantifying the performance of the monitoring device are quantified before the monitoring device is placed at the measurement location. The noise floor measurement is useful in the following applications: the manufacturing process, storage process, shipping process, and/or handling process may cause the monitoring device to become out of specification due to unforeseen circumstances. In an example embodiment, the noise floor measurement is generated by activating one or more light sources (e.g., 113 in fig. 1) of the monitoring device as the monitoring device is in normal operation. The light will diffuse through the material in the second label (label 911) and reach the one or more photodetectors via diffuse and/or specular reflection. The signal produced by the photodetector (e.g., 114 in fig. 1) is acquired by the monitoring device and demodulated, filtered, processed, and sent to the host device for signal-to-noise ratio and metric calculation.

In block 1406, it is determined: whether the calculated signal-to-noise ratio and/or metric is acceptable (e.g., greater than a predefined threshold). If not, the host device instructs the user that the monitoring is inoperable or ineligible and operation ceases (block 1408). In alternative embodiments, multiple noise floor runs may be required and processed before block 1406 is performed.

When the calculated signal-to-noise ratio and/or metric is acceptable, the process passes to block 1410, where a monitoring device is attached to the measurement location. For example, a second tag (e.g., tag 911) is removed to reveal the adhesive material and attach the monitoring device to the measurement location. The monitoring device and the host device then enter normal monitoring operation (block 1412).

FIG. 14B illustrates a second example method of determining an effective noise floor for a monitoring device. In the illustrated embodiment, the noise floor calculation is performed by the monitoring device prior to connection with the host device. Initially, a monitoring device is turned on at block 1400. For example, in one embodiment, the monitoring device is turned on by removing the first tab and pressing an indicator (e.g., tab 909 and indicator 910 in fig. 9D).

An effective noise floor measurement is performed by the monitoring device at block 1404. As with the monitoring device in normal operation, the noise floor measurement is determined by activating one or more light sources (e.g., light source 113 in fig. 1) in the monitoring device. The light will diffuse through the material in the second label (e.g., label 911 in fig. 9D) and be detected by one or more photodetectors (e.g., photodetector 114 in fig. 1) via diffuse and/or specular reflection. The signal produced by the photodetector is demodulated, filtered and processed by the monitoring equipment to calculate the signal-to-noise ratio and metric.

A determination is made at block 1406 as to whether the calculated signal-to-noise ratio and/or metric is acceptable (e.g., greater than a predefined threshold). If the calculated signal-to-noise ratio and/or metric is not acceptable, the operation ends at block 1408. In one embodiment, the monitoring device instructs the user to: the monitoring device is inoperable or rejected (e.g., by using at least one light source in the monitoring device to generate a flashing light in a particular pattern or pattern). In alternative embodiments, multiple noise floor runs may be required and processed before block 1406 is performed.

When the calculated signal-to-noise ratio and/or metric is acceptable, the process passes to block 1410, where a monitoring device is attached to the measurement location. In one embodiment, a second label (e.g., label 911 in fig. 9D) is removed to reveal the adhesive, and a monitoring device is attached to the measurement location. Then, at block 1402, the monitoring device connects to (e.g., wirelessly connects to) the host device. At block 1412, the monitoring device and the host device enter normal monitoring operation.

Fig. 15A depicts an example battery discharge curve. The x-axis represents battery voltage, and the y-axis may represent time, percentage, joules, voltage, or other values. The example battery discharge curve is divided into three charge voltage regions 1502, 1504, 1506. In the first charging voltage region 1502, the battery is fully charged or nearly fully charged. It can be observed that in region 1502, a small change (drop) in battery charge creates a larger change (drop) in battery voltage. Accordingly, a battery charge (e.g., a battery age) may be determined based on the battery voltage and/or other available parameters of interest (e.g., ambient temperature, circuitry loads, etc.).

In the second charging voltage region 1504, the change in battery charge does not affect (or does not substantially affect) the battery voltage. Thus, in the second charging voltage region 1504, the battery power is charged via one or more counters (or timers). In other words, region 1504 does not include: may be used to directly estimate observable or measurable parameters of the battery charge. Accordingly, one or more counters and/or information relating to other available parameters of interest (e.g., ambient temperature, circuitry load, etc.) are used to estimate battery charge.

As the battery discharges, the battery enters a third charge voltage region 1506, where the battery charge may again be estimated based on the observable battery voltage and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.). In an alternative embodiment, the first region 1502 and the second region 1504 may be merged into a single first/second non-observable charge voltage region 1508, and the calculations performed as described for the region 1504.

15B-15C illustrate example methods of estimating battery uptime of a battery in a monitoring device. The calculations in each method are performed via different functions in both closed and open loops. In closed loop, the battery voltage and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.) are used to directly estimate the battery charge. In open loop, the battery charge is estimated directly using one or more counters and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.).

In fig. 15B, at block 1510, a battery in a monitoring device is operating in a first charging voltage region (e.g., 1502 in fig. 15A). In an example embodiment, the battery charge is calculated by mapping the battery voltage and other parameters of interest (i.e., voltage, temperature, circuitry load, etc.) as a function of the normalized battery charge value. In one embodiment, the monitoring device measures battery voltage and other parameters of interest and communicates the measurements to the host device. The host device determines a normalized battery charge value.

In block 1512, the battery charge reaches a second charging voltage region (e.g., 1504 in fig. 15A), and the battery charge is estimated using one or more counters implemented in the monitoring device (e.g., in non-volatile memory). The counter value is communicated to the host device for processing, and the host device decrements the battery charge over time as a function of the counter value. In one embodiment, the one or more counters represent different modes of operation in the monitoring device. In an example embodiment, the first counter and the second counter may be implemented in a monitoring device. The first counter ("standby counter") is incremented whenever the monitoring device is in a standby mode (e.g., not connected to the host device). The second counter ("usage counter") is incremented each time the monitoring device is connected to the host device and is in operation. The standby mode and the usage mode may each have different power consumption levels, and therefore different standby counters and usage counters are used to record values proportional to the power consumption of the monitoring device. The standby counter and the usage counter may be incremented (or decremented) based on elapsed time and/or other available parameters of interest (e.g., ambient temperature, circuitry load, etc.) to account for discharge of the battery over time. The values of the standby counter and the usage counter are mapped by the host device to normalized battery energy values each time the battery operates in the second charging voltage region 1504.

Next, as shown at block 1514, the battery operates in a third charging voltage region (e.g., region 1506 in fig. 15A) and, similar to the first charging voltage region, the battery charge is calculated by a function that maps the battery voltage and other parameters of interest (i.e., voltage, temperature, circuitry loads, etc.). The battery remains in the third charging voltage region until the battery is fully discharged. When the battery is fully discharged, the host device may generate warnings and notifications that monitor low or exhausted power on the battery in the device.

FIG. 15C depicts a flowchart of an alternative method of estimating battery uptime of a battery in a monitoring device. In fig. 15C, the first charging voltage region and the second charging voltage region (e.g., region 1502 and region 1504 in fig. 15A) are merged into a merged charging voltage region (1508 in fig. 15A). The merged regions reduce the number of charging voltage regions and simplify the operation of estimating the battery usable time. Typically, the battery is in the first charging voltage region for a shorter period of time than in region 1504. Therefore, merging the first charging voltage region and the second charging voltage region into one merged region does not significantly affect the accuracy of the estimated value.

Initially, as shown at block 1516, the battery in the monitoring device is in the first/second charging voltage region and the battery charge is estimated using one or more counters implemented in the monitoring device. The counter value is communicated to the host device for processing. The host device reduces the battery power over time as a function of the counter value. In a non-limiting embodiment, the counters are implemented as a use counter and a standby counter. The values of the standby counter and the usage counter are mapped by the host device to normalized battery energy values whenever the battery is operating in the merged region.

Next, as shown in block 1514, the battery operates in a third charging voltage region and the battery charge is calculated by mapping the battery voltage and other parameters of interest (i.e., voltage, temperature, circuitry loads, etc.) to a function of the normalized battery energy value. The battery remains in the third charging voltage region until the battery is fully discharged. When the battery is fully discharged, the host device may generate: warnings and notifications of low or exhausted battery on a battery in a monitoring device.

The system of monitoring devices and host devices is a hybrid or distributed system. Some of the functions are implemented in a monitoring device and some of the functions are implemented in a host device. For example, in one embodiment, the monitoring device implements a counter and measures/calculates one or more parameters of interest (i.e., voltage, temperature, circuitry load, etc.). The monitoring device communicates values to a host device, where the host device performs open and closed loop fuel gauge calculations, displays a battery fuel gauge icon (see, e.g., fig. 13A), and issues warnings and/or notifications whenever the battery is low or depleted.

Fig. 16 shows a flow chart of a method of operating a monitoring device. Initially, as shown in block 1600, the monitoring device is turned on and an indicator light indicates that the monitoring device is in a standby mode. The standby mode represents a mode in which the monitoring device is waiting to be operable to connect to (e.g., wirelessly connect to) the host device. In one embodiment, a connection timer is started, at which time the connection timer monitors (e.g., counts down) a given period of time in which the monitoring device attempts to connect to the host device.

Then, at block 1602, the monitoring device attempts to connect to the host device. In one embodiment, the monitoring device repeatedly attempts to connect to the host device until the monitoring device successfully connects to the host device or until the connection timer times out (e.g., expires). When the monitoring device is successfully connected to the host device, processing branches to block 1606 where the monitoring device begins operation. In some embodiments, the connection timer is a watchdog timer that is reinitialized by the monitoring device firmware at selected times or periodically whenever the monitoring device is in normal operation. This ensures that the monitoring device processor and circuitry are only reset when a software and/or hardware failure occurs and the connection timer (i.e., watchdog timer) times out. If the monitoring device is in standby mode, the connection timer (i.e., watchdog timer) will timeout at a selected time or periodically, forcing the monitoring device to reset itself. This ensures that the monitoring device will always be operational (i.e. in standby or connected) and eliminates the need to reset and/or monitor the power switches in the device circuitry altogether, making a manual reset or power-up sequence by the user unnecessary in the event of a software or hardware failure. When the monitoring device fails to connect to the host device for a given period of time in block 1602 or loses connection with the host device in block 1606 due to a hardware or software failure, the connection timer (i.e., watchdog timer) times out and the method continues at block 1604, at which point a reset operation on the monitoring device is performed. The monitoring device processor and circuitry are reset and the indicator light is turned off. After a short period of time, the monitoring device processor and circuitry are reinitialized, the indicator light is turned on in block 1600, and the process in fig. 16 repeats itself.

The description and illustrations of one or more aspects provided in this application are not intended to limit or restrict the scope of the present disclosure, as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey ownership, and enable others to make and use the best mode of the claimed disclosure. The claimed disclosure should not be construed as limited to any aspect, example, or detail provided in this application. Various (structural and methodical) features, whether shown and described in combination or separately, are intended to be selectively included or omitted to produce embodiments of particular feature sets. Having provided a description and illustration of the present application, those skilled in the art may devise variations, modifications, and alternative aspects that fall within the spirit of the broader aspects of the general inventive concept as embodied in this application, without departing from the broader scope of the claimed disclosure.