Device, system and method for measuring physiological phenomena and physical activity in a subject
阅读说明：本技术 用于测量对象中的生理现象和身体活动的设备、系统和方法 (Device, system and method for measuring physiological phenomena and physical activity in a subject ) 是由 S·蒂亚加拉詹 高明武 于 2020-04-20 设计创作，主要内容包括：确定从身体穿戴传感器观察到的主导活动并准确地表征该活动的系统、设备和方法。各种实施例可以包括使用周期变换(PT)以及部分循环测量(PCM)计算来检测与来自一个或多个数据信道的数据区间相关联的主导活动。例如,在身体穿戴传感器(例如,胸部穿戴、肢体穿戴等)中,可能存在在时间、频率、幅值等上交叠的矛盾信号,并且该交叠数据可以以混合方式传达特定主导信息。本文描述的各种实施例试图通过使用分数循环和周期变换来确定来自任何信道的主导信息源。例如,根据各种实施例的周期变换可以提供可实时实现的特定变换方法,并且当与启发法一起使用以消除谐波/子周期信息时识别主导循环周期。因此,本文描述的各种实施例可以确定在患者体内或与患者一起发生的主导活动以及针对特定测量区间的可靠性量度(例如,信号质量指数)。(Systems, devices, and methods that determine a dominant activity observed from a body worn sensor and accurately characterize that activity. Various embodiments may include using Period Transformation (PT) and Partial Cycle Measurement (PCM) calculations to detect dominant activity associated with data intervals from one or more data channels. For example, in body worn sensors (e.g., chest worn, limb worn, etc.), there may be contradictory signals that overlap in time, frequency, amplitude, etc., and this overlapping data may convey certain dominant information in a mixed manner. Various embodiments described herein seek to determine the dominant information source from any channel by using fractional cycling and periodic transformations. For example, period transformation according to various embodiments may provide a particular transformation method that may be implemented in real-time, and identify dominant cycle periods when used with heuristics to eliminate harmonic/sub-period information. Thus, various embodiments described herein may determine a dominant activity occurring within or with a patient and a reliability measure (e.g., signal quality index) for a particular measurement interval.)
1. An apparatus (120) comprising:
an accelerometer (122) configured to receive accelerometer data;
a gyroscope (123) configured to receive gyroscope data; and
a microprocessor (129) configured to:
performing a partial loop analysis based on at least one of the accelerometer data and the gyroscope data to determine at least a top three loops;
performing a cycle transform (PT) -based selection of at least one top cycle observed from the partial loop analysis; and is
Heuristics are used to determine the most likely activity of the at least one top period.
2. The device of claim 1, wherein the most likely activity comprises at least one of: sudden motion, sustained motion, slow chest wall movement, deep chest wall movement, and severe vibration.
3. The apparatus of claim 1, wherein the PT-based selection is performed by:
(a) a hypothesis threshold (T);
(b) obtaining a signal X of length N via at least one of the accelerometer data and the gyroscope data;
(c) locating fractional cycle markers for each of the at least top three cycles;
(d) approximating Cycle Length (CL) for each cyclei) Sum amplitude (A)i) Wherein i is a cycle;
(e) approximating the expected period for each cycle;
(f) correlating the expected period with the fractional cycle marker for each cycle;
(g) making the expected period equal to one;
(h) removing the linear DC component by removing the projection onto a first expected period equal to one;
(i) make the expected period equal to CL1；
(j) Determination of A1Whether at least T% of the energy in X is contained;
(k) if A is1Containing at least T% of the energy in X, then accepting A1And CL1As a possible cycle;
(l) For each CLiAnd AiRepeating steps (i) - (k); and is
(m) based on the length of each Cycle (CL)i) And amplitude (A) per cyclei) To estimate the most likely cycle length.
4. The apparatus of claim 3, wherein if adjacent periods show a dominant characteristic due to variability within a measurement interval, a band of periods is formed and a center period within the band is designated as the most likely cycle length.
5. The apparatus of claim 1, wherein heuristics are based on at least one of: a particular amplitude range, a particular cycle length, data indicating continuity, data indicating duration of continuity, data indicating periodic nature, data indicating what channel of the sensor the signal is dominant in, and a degree of correlation to measurements made during the silence interval.
6. The device of claim 1, wherein the microprocessor is further configured to measure a Signal Quality Index (SQI) of an incoming signal associated with at least one of the accelerometer data and the gyroscope data using the presence or absence of periodic information associated with the partial loop analysis.
7. The device of claim 1, wherein the accelerometer and the gyroscope are housed in a wearable device.
8. The device of claim 7, wherein the wearable device is configured to be worn on the chest of a patient.
9. The device of claim 1, wherein the accelerometer data is received on-demand.
10. The device of claim 1, wherein the gyroscope data is received on-demand.
11. The device of claim 6, wherein a sudden change in SQI for a duration less than a first SQI threshold indicates a change in posture.
12. The device of claim 1, wherein the microprocessor is further configured to determine a Signal Quality Index (SQI) associated with the most likely activity.
13. The device of claim 1, wherein the heuristic comprises at least one of: a maximum power cycle length associated with the accelerometer data, a maximum power cycle length associated with the gyroscope data, an average power cycle length associated with the accelerometer data, an average power cycle length associated with the gyroscope data, a maximum significant estimation cycle length associated with the accelerometer data, a maximum significant estimation cycle length associated with the gyroscope data.
14. The apparatus of claim 1, wherein the partial loop analysis is performed by continuously mapping loops onto regions of maximum amplitude, minimum amplitude and zero crossing intervals and their periods.
15. The apparatus of claim 14, wherein the cycle is selected to be at least the top three cycles based on an amplitude threshold and a repeatability of the cycles.
16. The apparatus of claim 14, wherein a cycle is selected as at least the top three cycles based on the effective signal quality index.
17. The device of claim 16, wherein a baseline measurement noise magnitude is calculated and subtracted from a magnitude measurement associated with at least one of accelerometer data and gyroscope data.
18. A method, comprising:
receiving accelerometer data via an accelerometer of a wearable device (404);
receiving gyroscope data via a gyroscope of the wearable device (404);
performing a partial loop analysis (406) based on at least one of the accelerometer data and the gyroscope data to determine at least a top three loops;
performing a cycle transform (PT) -based selection (408) of at least one top cycle observed from the partial loop analysis; and is
Heuristics are used to determine a most likely activity of the at least one top period (410).
19. The method of claim 18, the most likely activity being respiration and the top cycle being a respiration rate.
20. The method of claim 18, wherein the most likely activity comprises at least one of: sudden motion, sustained motion, slow chest wall movement, deep chest wall movement, and severe vibration.
21. The method of claim 18, wherein the PT-based selection is performed by:
(a) a hypothesis threshold (T);
(b) obtaining a signal X of length N via at least one of the accelerometer data and the gyroscope data;
(c) locating fractional cycle markers for each of the at least top three cycles;
(d) approximating Cycle Length (CL) for each cyclei) Sum amplitude (A)i) Wherein i is a cycle;
(e) approximating the expected period for each cycle;
(f) correlating the expected period with the fractional cycle marker for each cycle;
(g) making the expected period equal to one;
(h) removing the linear DC component by removing the projection onto a first expected period equal to one;
(i) make the expected period equal to CL1；
(j) Determination of A1Whether at least T% of the energy in X is contained;
(k) if A is1Containing at least T% of the energy in X, then accepting A1And CL1As a possible cycle;
(l) For each CLiAnd AiRepeating steps (i) - (k); and is
(m) based on the length of each Cycle (CL)i) And amplitude (A) per cyclei) To estimate the most likely cycle length.
22. The method of claim 21, wherein a ratio of magnitudes of most likely cycle lengths is indicative of a signal quality index.
23. The method of claim 21, wherein the magnitude of the most likely cycle length and the presence of each magnitude relative to a region of interest determine a signal quality index for each estimation.
Technical Field
The present disclosure relates to devices, systems, and methods for measuring physiological phenomena and physical activity associated with a subject. In particular, the present disclosure relates to determining a dominant activity observed from a body worn sensor and accurately characterizing the activity.
Background
The healthcare industry is experiencing the emergence of wireless technology that can replace traditional health monitoring devices with wearable devices. These wearable devices can monitor physiological data related to the health of the patient. Thus, these wearable devices can greatly improve disease prevention, clinical care, and quality of life.
However, obtaining clinically accurate data from these wearable devices has proven to be quite difficult. For example, a wearable medical device may attempt to separate useful physiological information from data collected via a device worn on the body. To achieve this, the device may employ several components, including, for example, an accelerometer, gyroscope or gyroscope, a magnetometer, and/or a barometer. The physiological information may include, for example, pulse rate, blood pressure, respiration rate, and/or temperature. Among the physiological information, the respiration rate is typically not recorded or inaccurate, and therefore, is not available for adverse event detection.
For example, respiratory rate measurements may contain inaccuracies when based on periodic and non-periodic chest wall movements and vibrations. At any particular time, the mechanical motion seen from the periodic and aperiodic chest wall movements and vibrations can be defined as the detection of dominant visible activity. Also, for a resting patient, the sensor may indicate baseline respiratory activity as well as aperiodic sensor noise. Sudden non-periodic changes may occur in the sensor data when the patient rises to a standing or sitting position or otherwise changes posture. Also, a set of periodic oscillations can be seen as the patient moves, such as when the patient is walking. At any point in time, the ratio of dominant activity to other activity may determine a Signal Quality Index (SQI) measurement. Also, in order to take advantage of measurements such as respiration rate or other activity types, it may be necessary to accept measurements that can be reliably used via quality assurance of the quality index. Current models for respiration rate do not provide high quality SQI measurements and therefore do not provide clinically accurate respiration rate measurements. For example, when the dominant activity is not determined correctly, an accurate respiration rate cannot be calculated.
However, the activity level cannot often be determined accurately. For example, a medical quality algorithm may use measurements and/or contextual information that require a warranty or quality index to obtain acceptable measurements that can be reliably used. Furthermore, current models do not employ accurate or near accurate signal quality indices for use with each measurement.
Other challenges of existing approaches include problems associated with overlapping activities involving, for example, breathing, slow walking cycles, posture changes, and/or other motion-related artifacts. These challenges are witnessed by the inability to provide a one-to-one relationship that relates frequency domain information to a particular physiological phenomenon.
In addition to these, a problem is the fact that the breathing rate is often not recorded even when the patient's primary problem is respiratory condition. This is also despite the fact that abnormal breathing rate may be an important predictor of serious events such as sudden cardiac arrest or the admission to an Intensive Care Unit (ICU). Indeed, higher respiratory rates in patients may be associated with increased mortality. These and other disadvantages exist.
Advantageously, the present invention seeks to address these salient problems by measuring physiological phenomena and physical activity occurring at any point in the subject's body.
Disclosure of Invention
According to various embodiments, the present disclosure relates to systems, devices, and methods that determine a dominant activity observed from a body worn sensor and accurately characterize that activity. Various embodiments may include using Period Transformation (PT) and Partial Cycle Measurement (PCM) calculations to detect dominant activity associated with data intervals from one or more data channels.
For example, in body worn sensors (e.g., chest worn, limb worn, etc.), there may be contradictory signals that overlap in time, frequency, amplitude, etc., and this overlapping data may convey certain dominant information in a mixed manner. Various embodiments described herein seek to determine the dominant information source from any channel by using fractional cycling and periodic transformations. For example, period transformation according to various embodiments may provide a particular transformation method that may be implemented in real-time, and identify dominant cycle periods when used with heuristics to eliminate harmonic/sub-period information. Thus, various embodiments described herein may determine a dominant activity occurring within or with a patient and a reliability measure (e.g., signal quality index) for a particular measurement interval.
As an example, respiration rate measurements, such as measurements of various mechanical motions seen from periodic and aperiodic chest wall movements and vibrations, may be defined as the detection of dominant visible activity at any particular time. For a resting patient, the sensor may indicate baseline respiratory activity as well as aperiodic sensor noise. Sudden non-periodic changes appear in the sensor data when the patient rises or changes posture or turns. Again, a set of periodic oscillations can be seen as the patient undergoes a conventional slow or fast walk. At any time, the ratio of the dominant activity to the remaining activity may determine a Signal Quality Index (SQI) metric.
Various embodiments of the present disclosure may include an apparatus having: an accelerometer configured to receive accelerometer data; a gyroscope configured to receive gyroscope data; and a microprocessor configured to: performing a partial loop analysis based on at least one of the accelerometer data and the gyroscope data to determine at least a top three loops; performing a cycle transform (PT) -based selection of at least one top cycle observed from the partial loop analysis; and determining a most likely activity of the at least one top period using heuristics. In various embodiments, the most likely activity may include at least one of: sudden motion, continuous motion, slow chest wall motion, deep chest wall motion, and severe vibration.
According to various embodiments, the PT-based selection may be performed by: (a) a hypothesis threshold (T); (b) obtaining a signal X of length N via at least one of the accelerometer data and the gyroscope data; (c) locating fractional cycle markers for each of the at least top three cycles; (d) approximating Cycle Length (CL) for each cyclei) Sum amplitude (A)i) Wherein i is a cycle; (e) approximating an expected period for each cycle; (f) correlating the expected period with the fractional cycle signature for each cycle; (g) making the expected period equal to one; (h) removing the linear DC component by removing the projection onto a first expected period equal to one; (i) make the expected period equal to CL1(ii) a (j) Determination of A1Whether at least T% of the energy in X is contained; (k) if A is1Containing at least T% of the energy in X, then accepting A1And CL1As a possible cycle; (l) For each CLiAnd AiRepeating steps (i) - (k); and (m) based on each Cycle Length (CL)i) And amplitude (A) per cyclei) To estimate the most likely cycle length.
According to various embodiments, the partial loop estimation may occur simultaneously with or separately from the periodic transformation estimation.
In various embodiments, if adjacent periods show a dominant characteristic due to variability within the measurement interval, a band of periods is formed, and the center period within the band is designated as the most likely cycle length. Also, in various embodiments, the heuristics may be based on at least one of: a particular amplitude range, a particular cycle length, data indicating continuity, data indicating duration of continuity, data indicating periodic nature, data indicating in what channel of the sensor the signal is dominant, and a degree of correlation to measurements made in the silence interval. According to various embodiments, the heuristic may comprise at least one of: a maximum power cycle length associated with the accelerometer data, a maximum power cycle length associated with the gyroscope data, an average power cycle length associated with the accelerometer data, an average power cycle length associated with the gyroscope data, a maximum significant estimation cycle length associated with the accelerometer data, a maximum significant estimation cycle length associated with the gyroscope data.
Further, according to various embodiments, the microprocessor may use the presence or absence of period information associated with the partial loop analysis to measure a Signal Quality Index (SQI) of an incoming signal associated with at least one of the accelerometer data and gyroscope data. According to various embodiments, an abrupt change in the SQI for a duration less than the first SQI threshold may indicate a change in the posture.
In various embodiments, the accelerometer and gyroscope may be housed in a wearable device that may be worn on the chest of a patient, in the upper or lower chest area. The user may include a patient. In various embodiments, accelerometer data and/or gyroscope data may be received on demand and/or continuously. In various embodiments, sensor data may be pushed from the wearable device and/or pulled from the wearable device.
According to various embodiments, the microprocessor may determine a Signal Quality Index (SQI) associated with the most likely activity.
In various embodiments, the partial loop analysis may be performed by mapping the loop onto regions of maximum amplitude, minimum amplitude, and zero crossing intervals and periods thereof. The mapping may be continuous. Also, in various embodiments, the cycles may be selected to be at least the top three cycles based on the magnitude threshold and the repeatability of the cycles. Further, in various embodiments, a baseline measurement noise magnitude may be calculated and subtracted from a magnitude measurement associated with at least one of accelerometer data and gyroscope data.
Various embodiments may include: receiving accelerometer data via an accelerometer of a wearable device; receiving, via a gyroscope of the wearable device, gyroscope data; performing a partial loop analysis based on at least one of the accelerometer data and the gyroscope data to determine at least a top three loops; performing a cycle transform (PT) -based selection of at least one top cycle observed from the partial loop analysis; and determining a most likely activity of the at least one top period using heuristics. Various embodiments may include a top period of most likely respiratory activity and respiratory rate. Further, various embodiments may conclude that: the most likely activity comprises at least one of: sudden motion, sustained motion, slow chest wall movement, deep chest wall movement, and severe vibration.
Various embodiments may include performing the PT-based selection by: (a) a hypothesis threshold (T); (b) obtaining a signal X of length N via at least one of the accelerometer data and the gyroscope data; (c) locating fractional cycle markers for each of the at least top three cycles; (d) approximating Cycle Length (CL) for each cyclei) Sum amplitude (A)i) Wherein i is a cycle; (e) approximating an expected period for each cycle; (f) associating the expected period with the fractional loop signature for each loop; (g) making the expected period equal to one; (h) removing the linear DC component by removing the projection onto a first expected period equal to one; (i) make the expected period equal to CL1(ii) a (j) Determination of A1Whether at least T% of the energy in X is contained; (k) if A1 contains at least T% of the energy in X, then A is accepted1And CL1As a possible cycle; (l) For each CLiAnd AiRepeating steps (i) - (k); and (m) based on each Cycle Length (CL)i) And amplitude (A) per cyclei) To estimate the most likely cycle length.
In various embodiments, the implementation of any method or device-based algorithm may be in the form of hardware and/or firmware. For example, a low pass filter associated with a particular signal may filter or pass one or more signals having frequencies below a predefined threshold frequency. Advantageously, by placing the filter in hardware, the filter can be robust.
Various embodiments of the present disclosure provide devices, systems, and methods for measuring physiological phenomena and physical activity associated with a subject. In various embodiments:
drawings
The various embodiments of the disclosure, together with further objects and advantages, may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in the several figures of which like reference numerals identify like elements, and in which:
FIG. 1 depicts an example embodiment of a system including a device that measures physiological phenomena and physical activity associated with a subject;
FIG. 2 depicts an example embodiment of a system including a device that measures physiological phenomena and physical activity associated with a subject;
FIG. 3 depicts an example embodiment of a system including a device that measures physiological phenomena and physical activity associated with a subject;
FIG. 4 depicts a flowchart illustrating an example method of measuring physiological phenomena and physical activity associated with a subject in accordance with an embodiment of the invention;
FIG. 5 depicts an exemplary device housing technique to measure physiological phenomena and physical activity associated with a subject, in accordance with an embodiment of the present invention; and is
Fig. 6 depicts an exemplary device housing technique to measure physiological phenomena and physical activity associated with a subject, according to an embodiment of the invention.
Detailed Description
The following description is intended to convey a thorough understanding of the described embodiments by providing numerous specific example embodiments and details relating to devices, systems, and methods for measuring physiological phenomena and physical activity associated with a subject (e.g., measuring signal data, determining a signal quality index, and/or determining a most likely activity associated with sensor data). It should be appreciated, however, that the present disclosure is not limited to these specific embodiments and details, which are exemplary only. It should also be appreciated that in view of known systems and methods, those skilled in the art will appreciate the use of the present invention for its intended purposes and benefits in any number of alternative embodiments, depending on specific design and other needs.
Fig. 1 depicts an exemplary system 100 for use with devices, systems, and methods for measuring physiological phenomena and physical activity associated with a subject (e.g., measuring signal data, determining a signal quality index, and/or determining a most likely activity associated with sensor data). As shown in fig. 1, an example system 100 may include one or more wearable devices 120, one or more monitoring systems 140, and/or one or more mobile devices 150 connected by one or more networks 110.
For example, the network 110 may be one or more of a wireless network, a wired network, or any combination of wireless and wired networks. For example, the network 110 may include one or more of the following: a fiber optic network, a passive optical network, a cable network, an internet network, a satellite network, a wireless LAN, a global system for mobile communications ("GSM"), a personal communication service ("PCS"), a personal area network ("PAN"), a Wireless Application Protocol (WAP), a Multimedia Messaging Service (MMS), an Enhanced Messaging Service (EMS), a Short Message Service (SMS), a Time Division Multiplexing (TDM) based system, a Code Division Multiple Access (CDMA) based system, D-AMPS, Wi-Fi, fixed wireless data, IEEE 802.11b, 802.15.1, 802.11n, and 802.11g, a bluetooth network, or any other wired or wireless network for sending and receiving data signals.
Additionally, network 110 may include, but is not limited to, telephone lines, fiber optics, IEEE ethernet 902.3, wide area networks ("WANs"), local area networks ("LANs"), wireless personal area networks ("WPANs"), wireless body area networks ("WBANs"), or global networks such as the internet. Further, the network 110 may support an internet network, a wireless communication network, a cellular network, and the like, or any combination thereof. The network 110 may also include one network or any number of the above example types of networks, operating as independent networks or in cooperation with each other. Network 110 may utilize one or more protocols of one or more network elements to which they are communicatively coupled. The network 110 may convert other protocols to or from one or more protocols of the network device. Although the network 110 is depicted as a single network, it should be understood that the network 110 may include multiple interconnected networks, such as the Internet, a service provider's network, a cable television network, a corporate network, and a home network, in accordance with one or more embodiments.
Wearable device 120 may include, for example, one or more accelerometers 122, one or more gyroscopes 123, one or more photoplethysmography (PPG) sensors 124, one or more Electrocardiogram (ECG) sensors 125, one or more temperature sensors 126, one or more humidity sensors 127, location detection technology 128, and/or one or more microprocessors 129. The wearable device may also include other sensors 130, such as any magnetometer or barometer.
The wearable device 120 may, for example, receive signals and/or data indicating: vital signs (e.g., heart rate, respiration rate, temperature, blood pressure, blood glucose level, pulse oximetry, etc.), pain measurements, pressure measurements (e.g., via vital signs, skin conductance data, etc.), activity measurements (e.g., number of steps taken, number of floors climbed, type of activity, distance the user has moved, gait characteristics, gait speed, etc.). The wearable device 120 may be a limb-worn device (e.g., wrist, arm, ankle, leg, etc.), a chest-worn device, and/or a multi-position worn device. In various embodiments, the ECG device 110 and/or the wearable device 120 may include a wireless patch, a wired patch, a limb-worn device, and/or an implantable device. For example, the wearable device may be a chest worn patch as shown in fig. 5 and 6. For example, the chest-worn patches 502, 602 may be worn on the user/patient 500/600, as shown. The chest worn patches 502, 602 may have any shape and/or may be placed on the chest in any manner.
The monitoring system 140 may include an input/output interface 142, a processor 144, and/or a data storage device 146. Although the monitoring system 140 is depicted as a standalone system, the monitoring system 140 may be incorporated into, for example, the wearable device 120 and/or the mobile device 150.
The monitoring system 140 and/or the mobile device 150 may include, for example, one or more mobile devices, such as a Personal Digital Assistant (PDA), a tablet computer, and/or an e-reader (e.g., a PDA)KindlePlaybook, touch pad, etc.), wearable device (e.g., a game console, etc.)Glasses, AppleEtc.), telephone devices, smart phones, cameras, music playing devices (e.g., for example)Etc.), television, patient monitoring equipment (e.g., Philips)PhilipsPhilipsEtc.), set-top box devices, etc.
Monitoring system 140 and/or mobile device 150 may also include network-enabled computer systems and/or devices. As referred to herein, network-enabled computer systems and/or devices may include, but are not limited to: for example, any computer device or communication device, including, for example, a server, a network device, a Personal Computer (PC), a workstation, a mobile device, a telephone, a handheld PC, a Personal Digital Assistant (PDA), a thin client, a thick client, an internet browser, or other device. A network-enabled computer system may execute one or more software applications to receive data as input, for example, from an entity accessing the network-enabled computer system, process received data, transmit data over a network, and receive data over the network.
The monitoring system 140 and/or the mobile device 150 may include at least one Central Processing Unit (CPU) that may be configured to execute computer program instructions to perform various processes and/or methods. Processes and/or methods as described herein may be understood to refer to computer-executable software, firmware, hardware, and/or various combinations thereof. Note that where the processes and/or methods include software and/or firmware components, the software and/or firmware is configured to affect hardware elements of the associated system. It should also be noted that the processes and/or methods illustrated and described herein are intended to be examples. The processes and/or methods may be combined, integrated, separated, or duplicated to support various applications. Further, functions described herein as being performed at a particular process and/or method may be performed during one or more other processes and/or methods and by one or more other devices instead of, or in addition to, the functions performed as described. Further, the processes and/or methods may be implemented across multiple devices or other components, local or remote to each other. Additionally, processes and/or methods may be moved from one device and added to another device or may be included in both devices.
The monitoring system 140 and/or the mobile device 150 may include input/output interfaces 142, 152. The input/output interfaces 142, 152 may also include an antenna, a network interface that may provide or enable wireless and/or wired digital and/or analog interfaces to one or more networks, such as network 110, through one or more network connections, a power supply that provides appropriate Alternating Current (AC) or Direct Current (DC) to power one or more components of the system 100, and a bus that allows communication between the various components of the system 100. The input/output interfaces 142, 152 can include a display, which can include, for example, output devices such as a printer, a display screen (e.g., monitor, television, etc.), speakers, a projector, and so forth. Although not shown, the input/output interfaces 142, 152 may include one or more encoders and/or decoders, one or more interleavers, one or more circular buffers, one or more multiplexers and/or demultiplexers, one or more permuters and/or de-permuters, one or more encryption and/or decryption units, one or more modulation and/or demodulation units, one or more arithmetic logic units, and/or their components, and/or the like.
The input/output interface 142, 152 may comprise a bluetooth module or chipset having a bluetooth transceiver, chip, and antenna. The transceiver may send and receive information via the antenna and the interface. The chip may include a microprocessor that stores and processes piconet-specific information and provides device control functions. Device control functions may include connection creation, frequency hopping sequence selection and timing, power control, security control, polling, packet processing, and the like. Device control functions and other bluetooth-related functions may be supported using bluetooth APIs provided by platforms associated with the monitoring system 140 and/or the mobile device 150. Using the bluetooth API, applications stored on the monitoring system 140 and/or the mobile device 150 may be able to scan for other bluetooth devices, query local bluetooth adapters for paired bluetooth devices, establish RFCOMM channels, discover connections to and from other devices through services, and manage multiple connections. Bluetooth APIs used in the methods, systems, and devices described herein may include an API for Bluetooth Low Energy (BLE) to provide significantly lower power consumption and allow the monitoring system 140 and/or the mobile device 150 to communicate with BLE devices (e.g., the wearable device 120) having low power requirements.
The input/output interface 142, 152 may also include an NFC antenna and a Secure Element (SE). In one embodiment, the SE may be used for both digital and physically secure storage of sensitive data. The SE may include a computer processor or other computing hardware or software.
The input/output interfaces 142, 152 may implement industry standard NFC transmissions. For example, the input/output interfaces 142, 152 may enable two loop antennas to form an air-core transformer when placed in close proximity to each other through the use of magnetic induction. The input/output interface 142, 152 may operate at 13.56MHz or any other acceptable frequency. Further, the input/output interface 142, 152 may provide a passive communication mode in which the initiator device provides a carrier field allowing the target device to respond via modulation of an existing field. Additionally, the input/output interface 142, 152 may also provide an active communication mode by allowing alternate field generation by the initiator and target devices.
The input/output interface 142, 152 may deactivate the electromagnetic field for wireless broadcast and/or communication during certain (e.g., predefined and/or otherwise specified) intervals. For example, the input/output interface 142, 152 may deactivate the RF field while waiting for data. This deactivation may use miller-type coding with varying modulation (including 100% modulation). The deactivation may also use manchester encoding with varying modulation, including a modulation ratio of 10%. Additionally, the input/output interface 142, 152 may be capable of receiving and transmitting data simultaneously, as well as checking for potential collisions when the transmit and receive signal frequencies are different.
The input/output interfaces 142, 152 can utilize standardized transport protocols. Further, the input/output modules 142, 152 may be capable of utilizing transmission protocols and methods developed in the future using other frequencies or transmission modes. The input/output modules 142, 152 may also be backward compatible with existing technologies (e.g., RFID).
The monitoring system 140 and/or the mobile device 150 may include data storage devices 146, 156, including, for example, Random Access Memory (RAM) and Read Only Memory (ROM), which may be configured to access and store data and information and computer program instructions. The data storage devices 146, 156 may also include a storage medium or other suitable type of memory (e.g., such as RAM, ROM, Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic disk, an optical disk, a floppy disk, a hard disk, a removable cartridge, a flash drive, any type of tangible and non-transitory storage medium), where storage may include an operating system, application programs (including, for example, a web browser application, an email application), and the likeAnd/or other applications) files of the system as well as data files. The data storage devices 146, 156 may include electronic information, files, and documents stored in various ways, including, for example, flat files, index files, hierarchical databases, relational databases, such as from, for exampleA database created and maintained by the company's software,An Excel file,Access files or any other storage mechanism.
The monitoring system 140 and/or the mobile device 150 may also include, for example, processors 144, 154, which may be several processors, a single processor, or a single device with multiple processors. The processors 144, 154 may include any range of processing, such as, for example, a single chip microprocessor or a multi-chip processing unit. In various embodiments, processor 144 and/or processor 154 may execute a plurality of instructions and/or modules.
The monitoring system 140 and/or the mobile device 150 may include operationsAny mobile device that runs an iOS operating system (e.g., iPhone, iPad, Apple Watch, etc.) and/or that runs GoogleAny device operating a system, including, for example, runningSmart phones and other wearable mobile devices operating systems, such asSpectacles or Samsung GalaxySmart watch, running MicrosoftAny device of the mobile operating system and/or any other smart phone or similar device.
Although not shown, the mobile device 150, the monitoring system 140, the ECG device 110, and/or the wearable device 120 may be connected to a system associated with a general practitioner and/or an expert via the network 110 in order to transmit any received data and/or measurements associated with the monitoring described herein.
Fig. 2 depicts an exemplary system that may be used for patient monitoring as described herein. The example system 200 in fig. 2 may enable a medical facility to provide networked services and solutions, such as vital signs monitoring and analysis as described herein, to a patient, for example. As shown in fig. 2, system 200 may include user device 202 (e.g., a wearable device, an implantable device, etc., as described herein), network 204, front-end controlled domain 206, back-end controlled domain 212, and back-end 218. The front-end controlled domain 206 may include one or more load balancers 208 and one or more network servers 210. Back-end controlled domain 212 may include one or more load balancers 214 and one or more application servers 216.
The user device 202 may comprise some form of network-enabled computing device. As referred to herein, network-enabled computing devices may include, but are not limited to: for example, any computer device or communication device, including, for example, a wearable device, an implantable device, a thin client, a thick client, an internet browser, or other device. One or more network-enabled computing devices of the example system 200 may execute one or more software applications to enable, for example, network communications.
The computing device may comprise any computer device or communication device, including, for example, a server, a network device, a Personal Computer (PC), a workstation, a mobile device, a telephone, a handheld PC, a Personal Digital Assistant (PDA), a thin client, a thick client, an internet browser, or other device. These devices may include monitoring devices that receive data from wearable and/or implantable devices. One or more network-enabled computers of the example system 200 may execute one or more software applications to enable, for example, network communications.
The user equipment 202 may also be a mobile device. For example, the mobile device may include a mobile phone fromOf iPhone, iPod, iPad or any other mobile device running the iOS operating system of Apple, running of GoogleAny device that operates a system, including, for example, Google's wearable device, Google glasses, Microsoft-enabledAny device of the mobile operating system and/or any other smart phone or similar wearable mobile device.
The network 204 may be one or more of a wireless network, a wired network, or any combination of wireless and wired networks. For example, the network 204 may include one or more of the following: a fiber optic network, a passive optical network, a cable network, an internet network, a satellite network, a wireless LAN, a Body Area Network (BAN), a Wide Area Network (WAN), global system for mobile communications (GSM), Personal Communication Services (PCS), a Personal Area Network (PAN), D-AMPS, Wi-Fi, fixed wireless data, IEEE 802.11b, 802.15.1, 802.11n, and 802.11g, or any other wired or wireless network for sending and receiving data signals.
Additionally, network 204 may include, but is not limited to, telephone lines, fiber optics, IEEE Ethernet 902.3, a Wide Area Network (WAN), a Local Area Network (LAN), a Body Area Network (BAN), or a global network such as the Internet. Further, the network 204 may support an internet network, a wireless communication network, a cellular network, and the like, or any combination thereof. The network 204 may also include one network or any number of the above example types of networks, operating as independent networks or in cooperation with each other. Network 204 may utilize one or more protocols of one or more network elements to which they are communicatively coupled. Network 204 may convert other protocols to or from one or more protocols of the network device. Although network 204 is depicted as a single network, it should be understood that network 204 may include multiple interconnected networks, such as the Internet, a service provider's network, a cable network, a corporate network, and a home network, in accordance with one or more embodiments.
Front-end controlled domain 206 may be implemented to provide security for back-end 218. Load balancer(s) 208 may distribute workloads across multiple computing resources (e.g., computers, clusters of computers, network links, central processing units, or disk drives). In various embodiments, load balancer(s) 210 may distribute workload across, for example, web server(s) 216 and/or backend 218 systems. Load balancing aims at optimizing resource usage, maximizing throughput, minimizing response time, and avoiding overloading of any one resource. Using multiple components with load balancing rather than a single component may increase reliability through redundancy. Load balancing is typically provided by dedicated software or hardware, such as a multi-tier switch or Domain Name System (DNS) server process.
Load balancer(s) 208 may include software that monitors a port in which an external client (e.g., user device 202) is connected to access various services and/or devices of a medical facility, for example. The load balancer(s) 208 can forward the request to one of the application servers 216 and/or the backend 218 servers, and then the application servers 216 and/or the backend 218 servers can reply to the load balancer 208. This may allow the load balancer(s) 208 to reply to the user equipment 202 without the user equipment 202 knowing the internal separation of functionality. It may also prevent the user device from contacting the backend server directly, which may have security benefits by, for example, hiding the structure of the internal network and preventing attacks on unrelated services running on the backend 218 or on other ports.
The load balancer(s) 208 may use various scheduling algorithms to determine to which backend server to send a request. Simple algorithms may include, for example, random selection or rotation. The load balancer 208 may also take into account additional factors such as the reported load of the server, the recent response time, the up/down status (determined by some monitoring poll), the number of active connections, the geographic location, the capabilities, or how much traffic it has recently been allocated.
Load balancer 208 may be implemented in hardware and/or software. Load balancer(s) 208 may implement a number of features including, but not limited to: asymmetric loading; activation of priority: SSL offload and acceleration; distributed denial of service (DDoS) attack protection; HTTP/HTTPS compression; TCP unloading; TCP buffering; returning by the direct server; health examination; an HTTP/HTTPS cache; filtering the content; HTTP/HTTPS security; queuing is carried out preferentially; rate shaping; content aware switching; authenticating a client; programmed flow control; a firewall; an intrusion prevention system.
Web server(s) 210 may include hardware (e.g., one or more computers) and/or software (e.g., one or more applications) that deliver web content that may be accessed by, for example, a client device (e.g., user device 202) over a network such as the internet (e.g., network 204). Web server(s) 210 may communicate with user device 202 using, for example, hypertext transfer protocol (HTTP/HTTPs or sltp). The web pages delivered to the client devices may include, for example, HTML documents that may include images, style sheets, and scripts in addition to textual content.
A user agent (such as, for example, a web browser, web crawler, or native mobile application) may initiate a communication by requesting a particular resource using HTTP/HTTPs, and web server 210 may respond with the content of the resource or with an error message if not capable of doing so. The resource may be, for example, a file stored on the backend 218. Web server(s) 210 may also implement or facilitate receiving content from cardholder device 202, and thus cardholder device 202 may be able to, for example, submit a web form, including an upload file.
The web server(s) may also support server-side scripting using, for example, activeserverpages (asp), PHP, or other scripting languages. Thus, the behavior of the web server(s) 210 can be scripted in a separate file, while the actual server software remains unchanged.
Load balancer 214 may be similar to load balancer 208 as described above.
The application server(s) 216 may include hardware and/or software dedicated to efficiently executing processes (e.g., programs, routines, scripts) for applications supporting their applications. The application server(s) 216 may include one or more application server frameworks including, for example, Java application servers (e.g., Java platform Enterprise edition (Java EE), from Java platform Enterprise edition (Java EE))NET framework of company, PHP application server, etc.). Various application server frameworks may contain a comprehensive service layer model. Further, application server(s) 216 can serve as a set of components that are accessible, for example, by entities implementing system 200 through APIs defined by the platform itself. For web applications, these components may execute in the same operating environment as, for example, web server(s) 210, and application server 216 may support the building of dynamic pages. Application server(s) 216 may also implement services such as clustering, failover, and load balancing. In various embodiments, where application server(s) 216 are Java application servers, web server(s) 216 may behave like an extended virtual machine for running applications, transparently handling connections to databases associated with backend 218 on one side, and to web clients (e.g., client device 202) on the other side.
Backend 218 may include hardware and/or software to implement backend services of, for example, a medical facility and/or medical provider or other entity that maintains a distributed system similar to system 200. The backend 218 can be associated with various databases, including maintaining a database of, for example, medical information (e.g., patient data, vital sign data, classifier data, etc.). Backend 218 may also be associated with one or more servers that implement various services provided by system 200. The backend 218 may enable a healthcare facility and/or healthcare provider to implement various functions as shown and described herein.
Referring to fig. 3, a monitoring system and/or mobile device 300 may be provided, which may be similar (in whole or in part) to monitoring system 140 and/or mobile device 150. For example, fig. 3 illustrates an example architecture (e.g., hardware and software) for an example mobile device and/or monitoring system 300. Those skilled in the art will appreciate that the architecture of the mobile device and/or monitoring system 300 depicted in fig. 3 is merely an example in the context of the present disclosure, and that in some embodiments, additional, fewer, and/or different components may be used to implement similar and/or additional functionality. The mobile device and/or the monitoring system 300 may be embodied as a smartphone, tablet, mobile computing device, patient monitoring system, or the like. Also, in some embodiments, other types of devices may be used, including workstations, laptops, notebooks, and the like. In some embodiments, the mobile device and/or monitoring system 300 may be used to receive data from a wearable device (e.g., wearable device 120), further process data received from a wearable device (e.g., wearable device 120), generate alert and/or report data based on data received from a wearable device (e.g., wearable device 120), and/or transmit wearable device data and/or alert/report based on wearable device data.
The mobile device and/or monitoring system 300 may include at least two different processors, such as a baseband processor (BBP)304 and an application processor (APP) 308. The baseband processor 304 may primarily handle tasks related to baseband communications. The application processor 308 may process inputs and outputs and/or all applications except those directly related to baseband processing. The baseband processor 304 may comprise a dedicated processor for deploying functionality associated with a protocol stack, such as but not limited to a GSM (global system for mobile communications) protocol stack, and other functionality. The application processor 308 may include a multicore processor for running applications (including all or a portion of application software). Baseband processor 304 and application processor 308 may have respective associated memories (including Random Access Memory (RAM), flash memory, etc.), peripherals, and a running clock. The memories may also be referred to herein as non-transitory computer-readable media. Note that while depicted as residing in memory 310, all or a portion of the application software can be stored in memory 310, distributed among memories, or reside in other memories.
The baseband processor 304 can deploy functionality of a protocol stack to enable the mobile device and/or the monitoring system 300 to access one or more wireless network technologies, including WCDMA (wideband code division multiple access), CDMA (code division multiple access), EDGE (enhanced data rates for GSM evolution), GPRS (general packet radio service), Zigbee (e.g., based on IEEE 802.15.4), bluetooth, Wi-Fi (wireless fidelity, e.g., based on IEEE 802.11), and/or LTE (long term evolution), as well as variations thereof and/or other telecommunication protocols, standards, and/or specifications. The baseband processor 304 may manage radio communication and control functions including signal modulation, radio frequency shifting, and encoding. The baseband processor 304 may include or may be coupled to a radio (e.g., RF front end) 302 and/or a GSM (or other communication standard) modem, as well as analog and digital baseband circuitry (ABB, DBB, receive ground). Radio 302 may include one or more antennas, transceivers, and power amplifiers to enable receiving and transmitting signals of multiple different frequencies to enable access to a cellular (and/or wireless) network. Analog baseband circuitry may be coupled to radio 302 and provide an interface between the analog and digital domains of the GSM modem. The analog baseband circuitry may include circuitry including analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), as well as control and power management/distribution components and audio codecs to process analog and/or digital signals received indirectly via the application processor 308 or directly from a User Interface (UI)318 (e.g., microphone, headphones, alarm tones, vibrator circuitry, touch screen, etc.). The ADC may digitize any analog signal for processing by digital baseband circuitry.
The digital baseband circuitry may deploy the functionality of one or more levels (e.g., layer 1, layer 2, etc.) of the GSM protocol stack and may include a microcontroller (e.g., a microcontroller unit or MCU, also referred to herein as a processor) and a digital signal processor (DSP, also referred to herein as a processor) that communicate over a shared memory interface (the memory including data and control information and parameters indicating actions to take on data processed by the application processor 308). The MCU may be embodied as a RISC (reduced instruction set computer) machine running a real-time operating system (RTIOS), where the core has a number of peripherals (e.g., circuits packaged as integrated circuits), such as RTC (real time clock), SPI (serial peripheral interface), I2C (inter-integrated circuit), UART (universal asynchronous receiver/transmitter), IrDA (infrared data association) based devices, SD/MMC (secure digital/multimedia card) card controllers, keyboard scan controllers and USB devices, GPRS encryption modules, TDMA (time division multiple access), smart card reader interfaces (e.g., universal digital/multimedia cards). For receive side functions, the MCU may instruct the DSP to receive, for example, in-phase/quadrature (I/Q) samples from the analog baseband circuitry, and perform detection, demodulation, and decoding, and report back to the MCU. For transmit side functions, the MCU presents transmittable data and auxiliary information to the DSP, which encodes the data and provides it to analog baseband circuitry (e.g., for conversion to an analog signal by a DAC).
The application processor 308 may operate under the control of an Operating System (OS) that enables a number of user applications, including, for example, application software that supports vital sign calculation, alerting, and/or reporting. The application processor 308 may be embodied as a system on a chip (SOC) and support a number of multimedia-related features, including internet/cloud-based access functionality for accessing one or more computing devices of the cloud(s) coupled to the internet. For example, the application processor 308 may execute communication functions of application software (e.g., middleware that may include a browser having or operable in association with one or more Application Program Interfaces (APIs)) to enable access to a cloud computing framework or other network to provide remote data access/storage/processing, and through cooperation with an embedded operating system, access to calendars, location services, user data, public data, vital sign data, contact data, and the like.
For example, in some embodiments, vital sign calculations, alerts, and/or reports may operate using cloud computing services, wherein processing of raw and/or derived parameter data received at the mobile device and/or monitoring system 300, either indirectly or directly from a wearable device (e.g., wearable device 120), may be effected by one or more devices of the cloud(s), and a trigger signal (to trigger feedback) may be communicated from the cloud(s) (or other devices) to the mobile device and/or monitoring system 300, which in turn may activate feedback internal to the mobile device and/or monitoring system 300 or relay the trigger signal to other devices.
The application processor 308 may include a processor core (advanced RISC machine or ARM) and also includes or may be coupled to a multimedia module (for decoding/encoding pictures, video, and/or audio), a Graphics Processing Unit (GPU), a communication interface 328, and a device interface. Communication interface 328 may include a wireless interface, including a Bluetooth (BT) (and/or Zigbee and others, in some embodiments) module that enables wireless communication with any system component.
The communication interface 328 may include a Wi-Fi module for interfacing with a local 802.11 network according to corresponding ones of the application software. The application processor 308 may include or be coupled to a Global Navigation Satellite System (GNSS) receiver 330 for enabling access to a network of satellites to, for example, provide position coordinates. In some embodiments, the GNSS receiver 330 associated with the GNSS functions in the application software may collect contextual data (time and location data, including location coordinates and altitude) to determine location data associated with the mobile device and/or the monitoring system 300. Note that although described as GNSS receiver 330, other indoor/outdoor positioning systems may be used, including those based on cellular network signals and/or Wi-Fi triangulation.
A device interface coupled to the application processor 308 may include a user interface 318, such as a display screen. The display screen may be embodied in one of several available technologies, including LCD or liquid crystal display (or variants thereof, such as Thin Film Transistor (TFT) LCD, in-plane switching (IPS) LCD), Light Emitting Diode (LED) based technologies (such as organic LEDs (oled), active matrix oleds (amoled), retina or haptic based technologies, or virtual/augmented reality technologies. For example, the user interface 318 may present visual feedback in the form of messages (e.g., alphanumeric) and/or symbols/graphics (e.g., warning or alert icons, flashing screens, etc.) and/or flashing Lights (LEDs). In some embodiments, user interface 318 may be configured as a keyboard, microphone, speaker, headphone connector, I/O interface (e.g., USB (universal serial bus)), SD/MMC card, and other peripheral devices in addition to or in place of a display screen. For example, a speaker may be used to audibly provide feedback, and/or user interface 318 may include a vibration motor that provides vibratory feedback to the user. One or any combination of visual, audible, or tactile feedback may be used, and as previously described, a change in intensity of the feedback format may be used.
An image capture device (image capture) 326 may also be coupled to the application processor 308. The image capture device 326 may include an optical sensor (e.g., a Charge Coupled Device (CCD) or a Complementary Metal Oxide Semiconductor (CMOS) optical sensor). The image capture device 326 may be configured to capture and/or receive medical imaging, such as ultrasound and/or other scans.
Power management device 322 may control and manage the operation of battery 324 and/or other power sources. The components described above and/or depicted in fig. 3 may share data through one or more buses, such as via a data bus 332. Those skilled in the art will appreciate that in the context of the present disclosure, variations of the above may be deployed in some embodiments to achieve similar functionality.
The application processor 308 may run and/or execute application software 314 and may include a vital signs application 315, a language processing application 316, and/or a feedback application 317.
A method of utilizing the system components described herein is described with reference to fig. 4. For example, the method may begin at block 402. At block 404, a wearable device (such as wearable device 120) may receive raw sensor data, such as accelerometer data, gyroscope data, magnetometer data, barometer data, PPG data, ECG data, etc., via one or more sensors on the wearable device.
At block 406, initial processing may occur and the partial loop analysis may determine at least the top three loops at any point in time. As an example, the initial processing may include smoothing and downsampling the raw sensor data (e.g., the input signal or a time series of measurements). Partial loop analysis may include locating fractional loop markers (e.g., waveform peaks-one to three scales, waveform valleys-one to three scales, and/or zero crossing portions). The partial loop analysis may include loop lengths and amplitudes approximating at least three loops (e.g., a1, CL1, a2, CL2, A3, CL 3). The partial loop analysis may assume three maximum periods in the body worn sensor. The partial loop analysis may approximate expected periods (e.g., P1, P2, P3) and associate them with the underlying mathematical model.
The mathematical model of the bottom layer of the chest worn sensor can be expressed as:
{y(n)}＝{xr(n)}+{xg(n)}+{xa(n)}+{w(n)} (1)
wherein the content of the first and second substances,
observed chest sensor signal (y (n) } ═ c
{ xr (n) } ═ measured respiratory signal
{ xg (n) } measured gait signal
{ xa (n) } ═ measured artifact signal, and
{ w (n) } white noise signal
At block 408, system components (e.g., the wearable device and/or the implantable device and/or a device in communication with the wearable and/or implantable device) may perform a cycle-based selection of at least one top cycle observed from the respiratory cycle analysis. Using the cycle-based selection, a digital signal and/or a time series of physiological data can be detected. For example, a periodic transformation may decompose a sequence into a sum of periodic sequences by projecting onto a set Pp of periodic subspaces, which may result in leaving a residual where the period has been removed. The resulting data representation may then be period linear, rather than frequency or scale linear.
By way of example, the selection based on the periodic transformation may include estimating a most likely cycle length based on the magnitude (e.g., a1, a2, A3) and the available cycle length (e.g., CL1, CL2, CL 3). In this manner, the period-based selection may include determining whether adjacent periods show a dominant characteristic due to variability within the measurement interval, and if so, determining that a band of periods is formed and designating the center period within the band as the most likely cycle length.
As an example, in the case where there are three desired periods, the period may be set to 1 (e.g., P1 ═ 1). The linear DC component may then be removed by removing the projection onto the period (e.g., P1 ═ 1). Next, the period may be set to the cycle length (e.g., P1 ═ CL1), and the device may check whether the projection (e.g., a1) includes at least a threshold (e.g., T) percentage of the energy in the signal (e.g., X). If so, the device may accept the projection (e.g., A1) and cycle length (e.g., CL1) as possible cycles that may exist. This may be repeated for each identified cycle (e.g., CL2, CL 3). The determined data, variability within the measurement interval, and the period band may then be used to estimate the most likely cycle length based on the available amplitude and cycle length.
Additionally, the most likely activity within a particular time may be calculated using a maximum power cycle length of the filtered time series of sensor data, an average power cycle length of the filtered time series of sensor data, and a maximum significant estimated cycle length of the filtered time series of data.
At block 410, system components, such as the wearable device and/or the implantable device and/or a device in communication with the wearable and/or implantable device, may determine the most likely activity of at least one top period observed from the partial loop analysis using heuristics. The heuristics may include data and/or data features related to characteristics of the breathing signal, characteristics of the gait signal, characteristics of the motion artifact signal, and/or characteristics of the baseline electronic noise. By way of example, the data and/or data characteristics that may be representative of characteristics of the respiration signal may include continuity, a particular amplitude range, one or more particular cycle lengths (periods), pseudo-periodic properties, dominance in several, if not all, channels of the sensor, and correlation with measurements made in the silence interval. By way of example, the data and/or data characteristics that may be representative of the characteristics of the gait signal may include a continuity of short duration, a particular amplitude range, one or more particular cycle lengths (periods), a pseudo-period, and a difference from measurements taken at the silence interval. By way of example, the data and/or data characteristics that may be characteristic of the motion artifact signal may include lack of continuity, a particular amplitude range, one or more particular cycle lengths (periods) (non-periods), dominance in several, if not all, channels of the sensor, and non-correlation with measurements made in the silence interval. Artifacts, data and/or data features that may be characteristic of baseline electronic noise may include: lack any period of time, non-correlation between segments, and bounded magnitude.
At block 412, a system component (e.g., a wearable device and/or an implantable device and/or a device in communication with the wearable and/or implantable device) may measure a signal quality index of the input signal using the presence or absence of periodic information associated with the partial loop analysis. By way of example, data characteristics present in the heuristic may be used to determine a signal quality index. Or more precisely, the data characteristics in each active band may be defined by information continuity, band limitation and/or absence of noise. The signal quality index may identify the insubstantial as defined by sampling content outside the region of interest in the time and/or frequency domain. By way of example, activities such as walking and/or climbing stairs may result in higher frequency content and may be out of band of respiratory activity (e.g., phase 1). Similarly, complete inactivity due to lack of sensor adhesion and/or contact may result in DC content (e.g., event 2). Both types of events may be located outside the region of interest.
At block 414, system components (e.g., the wearable device and/or the implantable device and/or a device in communication with the wearable and/or implantable device) may determine user characteristics and/or device status information using the signal quality index. For example, a sudden change in the signal quality index may indicate a possible change in attitude. As another example, a sudden increase in DC may push the signal quality index low and will indicate device removal and/or sensor error(s).
The method may end at block 416.
These examples are merely illustrative, and the transaction card may be reprogrammed in accordance with any of the data described herein.
It should also be noted that the systems and methods described herein may be tangibly embodied in one of more physical media, such as, but not limited to, a Compact Disc (CD), a Digital Versatile Disc (DVD), a floppy disk, a hard drive, Read Only Memory (ROM), Random Access Memory (RAM), and other physical media capable of storing software, or a combination thereof. Further, the figures illustrate various components (e.g., servers, computers, processors, etc.) separately. The functions described as being performed at various components may be performed at other components, and the various components may be combined or separated. Other modifications may also be made.
The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. As may be apparent, many modifications and variations are possible without departing from the spirit and scope thereof. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, may be apparent from the foregoing representative descriptions. Such modifications and variations are intended to be within the scope of the appended representative claims. The disclosure is to be limited only by the terms of the appended representative claims, along with the full scope of equivalents to which such representative claims are entitled. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. Various singular/plural permutations may be expressly set forth herein for clarity.
It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent may be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the word "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and, for example, the words "a" or "an" (e.g., "a" or "an" should be interpreted to mean "at least one" or "one or more"); the same holds true for the use of "a" or "the" to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Moreover, in those instances where a convention analogous to "at least one of A, B and C, etc." is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having only a, only B, only C, A and B together, a and C together, B and C together, and/or A, B and C together, etc.). In those instances where a convention analogous to "A, B or at least one of C, etc." is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include, but not be limited to, systems having only a, only B, only C, A and B together, a and C together, B and C together, and/or A, B and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to encompass the possibility of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" may be understood as a possibility to include "a" or "B" or "a and B".
The foregoing description, along with its associated embodiments, has been presented for purposes of illustration only. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed embodiments, as would be appreciated by those skilled in the art from the foregoing description. For example, the steps described need not be performed in the same order of discussion or with the same degree of separation. Also, various steps may be omitted, repeated, or combined as desired to achieve the same or similar objectives. Accordingly, the present invention is not limited to the embodiments described above, but instead is defined by the appended claims in view of their full scope of equivalents.
In the preceding specification, various preferred embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.