阅读说明：本技术 用于确定患者体温的设备、系统及方法 (Apparatus, system and method for determining a body temperature of a patient ) 是由 南达库马尔·塞尔瓦拉伊 加布里埃尔·纳拉坦比 于 2020-04-06 设计创作，主要内容包括：公开了一种用于基于皮肤温度和传感器环境空气温度确定患者体温的无线可穿戴传感器设备、方法及非暂时性计算机可读介质。(A wireless wearable sensor apparatus, method, and non-transitory computer-readable medium for determining a patient's body temperature based on a skin temperature and a sensor ambient air temperature are disclosed.)

1. A method of determining a body temperature of a patient, comprising:

measuring a first temperature value at a skin surface of a patient's body by a first sensor;

measuring, by a second sensor, a second temperature value of a sensor ambient air temperature at the first sensor;

determining a core body heat exchange at the skin surface on the patient body using the first temperature value and the second temperature value;

determining the patient temperature by using the core body heat exchange;

controlling an absolute amplitude level of the core body heat exchange; and is

And outputting the body temperature of the patient.

2. The method of claim 1, further comprising: ambient temperature fluctuations are eliminated from the skin temperature values by using an adaptive filter.

3. The method of claim 1, wherein the core body heat exchange at the skin surface on the body is determined by subtracting an ambient filter output from the skin temperature value.

4. The method of claim 1, wherein the controlling the absolute amplitude level value of the core body heat exchange comprises subtracting an AC offset of the core body heat exchange and adding a DC calibration value that transforms time-varying trend changes of the core body heat exchange to an absolute measure comparable to a standard patient temperature measurement.

5. The method of claim 4, wherein the core body heat exchange comprises at least one of:

in the case of the first calibration, the AC offset is the value of the core body heat exchange during the temperature sensor stabilization, or

In the case of recalibration, the AC offset is the value of the core body heat exchange at the time of the recalibration request.

6. The method of claim 1, further comprising:

the settling time flag (ts _ flag) and the calibration flag (cal _ flag) are initialized using initial values.

7. The method of claim 6, wherein the initial values of the settling time flag and the calibration flag are zero.

8. The method of claim 1, wherein the input values of the first temperature value f (n) and the reference second temperature value d (n) are passed through an adaptive filter to produce an adaptive filter output y (n).

9. The method of claim 8, wherein the updating of the filter coefficients is performed by minimizing the error according to e (n) -d (n) -y (n): ,

wherein d (n) is a desired reference input value for the second temperature value, and

wherein said y (n) is said adaptive filter output.

10. The method of claim 8, wherein the core body heat exchange T _ x between the skin body surface and core body is determined from T _ x (n) (f) -y (n) by subtracting the adaptive filter output from the first temperature values,

wherein f (n) is the first temperature value, and

wherein said y (n) is said adaptive filter output.

11. The method of claim 1, wherein the patient temperature output is invalidated with a unique digital code until a stabilization flag (ts _ flag) and the calibration flag (cal _ flag) begin or change from 0 to 1.

12. The method of claim 1, wherein the patient temperature output is the same as the output of the input calibration temperature value until it is determined that the temperature sensor has stabilized to a steady state or until a desired stabilization duration has elapsed.

13. The method of claim 6, further comprising outputting the patient temperature to a display.

14. A wireless sensor device for temperature monitoring, comprising:

a first sensor to measure a first temperature value at a skin surface of a patient's body;

a second sensor that measures a second temperature value of a sensor ambient air temperature at the first sensor;

a computing device comprising a memory and a processor, wherein the computer device receives the first and second temperature values and executes an application program stored in the memory by the processor to:

determining a core body heat exchange at the skin surface on the patient body using the first temperature value and the second temperature value,

determining the patient's body temperature by using the core body heat exchange, and

controlling an absolute amplitude level of the core body heat exchange; and

a display device to display the patient's temperature.

15. The wireless sensor device of claim 14, wherein the computing device further implements the application to:

eliminating ambient temperature fluctuations from the skin temperature values;

determining a body core heat exchange at the skin surface on the patient body; and is

Controlling an absolute magnitude of the bulk core heat exchange.

16. A non-transitory computer-readable medium storing executable instructions that, in response to execution, cause a computing device of a wireless sensor device to perform operations comprising:

measuring, by a first sensor, a first temperature value at a skin surface on the patient's body;

measuring, by a second sensor, a second temperature value of a sensor ambient air temperature at the first sensor;

determining a core body heat exchange at the skin surface on the patient body using the first temperature value and the second temperature value;

determining the patient temperature by using the core body heat exchange;

controlling an absolute amplitude level of the core body heat exchange; and outputting the patient temperature.

17. The non-transitory computer-readable medium of claim 16, further comprising: ambient temperature fluctuations are eliminated from the skin temperature values by using an adaptive filter.

18. The non-transitory computer-readable medium of claim 16, wherein the core body heat exchange at a skin surface on the body is determined by subtracting an ambient filter output from the skin temperature value.

19. The non-transitory computer readable medium of claim 16, wherein the controlling the absolute amplitude level value of the core body heat exchange comprises subtracting an AC offset of the core body heat exchange and adding a DC calibration value that converts time-varying trend changes of the core body heat exchange to an absolute measure comparable to a standard patient temperature measurement.

20. The non-transitory computer-readable medium of claim 19, wherein the core body heat exchange comprises at least one of:

in the case of the first calibration, the AC offset is the value of the core body heat exchange during the temperature sensor stabilization, or

In the case of recalibration, the AC offset is the value of the core body heat exchange at the time of the recalibration request.

Technical Field

The present application relates to an apparatus, system, and method for determining a patient's body temperature based on a wearable sensor that measures a first temperature at a body skin surface and a second temperature from sensor ambient air.

Background

Core temperature is the temperature measured at deep tissues of the body such as the abdominal cavity, thoracic cavity, and cranial cavity. The core temperature is regulated by hypothalamic endotherms of the brain. The gold standard for measuring core temperature is the pulmonary artery or esophageal catheter. However, oral thermometers are generally suitable for measuring patient body temperature in a clinical setting and require proper placement in a sublingual pocket while keeping the mouth closed. A disadvantage of oral temperature measurement is that oral temperature measurement is an approximation of core temperature and that measuring temperature at the oral site may be affected by other external factors including, for example, drinking hot/cold beverages, eating and smoking. The axillary position, commonly referred to as the axillary region, is another popular option, particularly for pediatric patients, where the temperature is lower than the core body temperature of the patient. Furthermore, armpit temperature measurement is unreliable because it is sensitive to the correct placement of the underarm tip, the correct closure of the arm to the body during temperature measurement, and the presence of sweat and hair (if an adult). At the same time, rectal temperature is the least popular choice due to inconvenience and compliance, and is higher than the core body temperature. In all of the direct mode patient temperature measurement options described above, the output temperature is an unadjusted direct temperature measurement from the measurement site to which a single thermometer or sensor probe is coupled. Conventional patient temperature measurement methods have unique limitations related to inherent location-related variability, environmental impact, and convenience/utility, and are not suitable for continuous, uninterrupted patient monitoring. Thus, a new low power wireless wearable sensor can alleviate the above problems and conveniently provide continuous temperature measurement without disturbing the patient or user.

In one case, a single temperature sensor, such as a thermistor embedded in a wearable sensor, is applied on the skin surface of any part of the patient's body, and a measurement of the local skin temperature (skinntemp) can be provided by converting the measured resistance directly unadjusted to temperature by adjusting the thermistor coefficient according to the resistive properties or an additional algorithm that takes into account the thermal properties of the sensor. The skinntemp measured at the body surface using a single thermistor is temperature-variable, i.e. strongly influenced by local blood perfusion and the external environment. Therefore, the skinntemp may exhibit large fluctuations due to external factors (such as clothes covering the measurement site and environmental changes in the surrounding environment of the user). Thus, the skinntemp of a wearable sensor may be less useful for clinical patient monitoring and patient intervention in a hospital. This limitation of skinntemp and conventional patient measurement methods makes it desirable to have a wearable sensor that can continuously measure accurate patient body temperature without manual measurement errors and environmental effects. Therefore, a solution to overcome the above problems is strongly desired. The present application addresses this need by proposing a wearable sensor that measures the sensor ambient air temperature in addition to the skinntemp, and an algorithm that adjusts skinntemp to produce body temperature (see hereinafter body temp) by eliminating the effect of the sensor ambient air temperature (AmbTemp) (comparable to the standard patient temperature).

Disclosure of Invention

A method of determining a body temperature of a patient is disclosed. In an embodiment, the method comprises: measuring a first temperature value at a skin surface of a patient's body by a first sensor; measuring, by a second sensor, a second temperature value of a sensor ambient air temperature at or near the first sensor; determining heat exchange at a skin surface on a patient's body; determining a patient temperature by using the first temperature value, the second temperature value, and the heat exchange; and outputs the patient's temperature.

A wireless wearable sensor device for temperature monitoring is disclosed. In an embodiment, a wireless wearable sensor apparatus includes: a first sensor that measures a first temperature value at a skin surface on a patient's body; a second sensor that measures a second temperature value of a sensor ambient air temperature at or near the first sensor; a computing device comprising a memory and a processor, wherein the computer device receives the first and second temperature values and executes, by the processor, an application program stored in the memory to determine a patient temperature; and a display device that displays the body temperature of the patient.

A non-transitory computer-readable medium storing executable instructions that, in response to execution, cause a computing device of a wireless wearable sensor device to perform operations is disclosed. In an embodiment, a non-transitory computer-readable medium stores executable instructions that, in response to execution, cause a computing device of a wireless wearable sensor device to perform operations comprising: measuring, by a first sensor, a first temperature value at a skin surface on a patient's body; measuring, by a second sensor, a second temperature value of the sensor ambient air temperature at the first sensor; determining heat exchange at a skin surface on a patient's body; determining a patient temperature by using the first temperature value, the second temperature value, and the heat exchange; and outputs the patient's temperature.

Drawings

The drawings illustrate several embodiments of the invention and together with the description serve to explain the principles of the invention. Those of ordinary skill in the art will readily recognize that the embodiments illustrated in the figures are merely exemplary and are not intended to limit the scope of the present application.

Fig. 1 shows a wireless wearable sensor device for health monitoring according to an embodiment.

Fig. 2 shows a flow chart for determining a body temperature of a patient.

Fig. 3 shows a flow chart of a patient temperature prediction algorithm.

Fig. 4a shows a graph depicting the SkinTemp time curve recorded from a group of participants immediately after adhesive sensor application.

Fig. 4b shows a graph depicting the difference in consecutive skinntemp values as their rate of change.

FIG. 5a shows a graph depicting the BodyTemp output plotted together with the SkinTemp and AmbTemp from the same sample record for comparison.

Fig. 5b shows a graph depicting body temp output plotted along with skinntemp, AmbTemp, and reference oral thermometer temperature (OralTemp) from the same sample record for comparison.

Fig. 6 illustrates a block diagram of a computing device.

Detailed Description

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it is to be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. In the description, details of features and techniques that are well known to those skilled in the art may be omitted to avoid unnecessarily obscuring the presented embodiments.

References in the specification to "one embodiment," "an example embodiment," etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with or in connection with other embodiments whether or not explicitly described.

Patient temperature prediction derives from the physiology of the human thermoregulatory mechanisms, which balance the internal metabolic heat production with the external heat lost from body surfaces through blood perfusion, radiation, conduction, and convection processes. The human thermoregulatory system attempts to eliminate fluctuations in atmospheric changes for normal operating environmental conditions, such as ambient temperatures between 61 ° F and 104 ° F, and to maintain a constant internal core temperature. Thus, the relationship between the ambient temperature and the change in core temperature or patient temperature may remain constant for a normal range of ambient conditions.

The above physiological relationships can be constructed as a mathematical model as given below.

qc(T_b-T_s)＝hA(T_s-T_a) (1)

Wherein, T_bIs body temp; t is_sIs SkinTemp; t is_aIs AmbTemp; q is the blood flow rate; c is the specific heat of the blood; h is the heat transfer coefficient; a is the body surface area.

Simplifying the above equation (1),

based on the above theoretical framework, the body temp prediction algorithm allows the use of accurate independent temperature sampling at two different interfaces of the skin surface and the sensor ambient air to cancel out environmental changes from the skinntemp, estimate heat transfer or exchange from the core body to the chest skin surface, and convert the temperature output measure to a similar measure to that of the core temperature for comparison.

Fig. 1 shows a wireless wearable sensor device 100 for measuring a first temperature at a skin surface of a body and a second temperature from sensor ambient air. The wireless wearable sensor device 100 or wearable device includes a sensor 102, a processor 104 coupled to the sensor 102, a memory 106 coupled to the processor 104, a wireless wearable sensor device application 108 coupled to the memory 106, and a transmitter 110 coupled to the wireless wearable sensor device application 108.

The wireless wearable sensor device 100 is attached to a user to measure a first temperature at a body skin surface and a second temperature from sensor ambient air. The sensors 102 each include, but are not limited to, a thermistor. The sensor 102 obtains temperature data from the body skin surface and sensor ambient air surrounding the sensor, which is transferred to the memory 106 and then to the wireless wearable sensor device application 108 via the processor 104. The memory 106 may be a flash memory. The processor 104 executes the wireless wearable sensor device application 108 to process and obtain information about the user's health. This information may be sent to the transmitter 110 and to another user or device for further processing, analysis, and storage. That is, the transmitter 110 may transmit various temperature data to a remote device/server (e.g., smartphone, cloud-based server) for processing, analysis, and storage. The transmitter 110 may be a Bluetooth Low Energy (BLE) transceiver. Alternatively, the wireless wearable sensor device 100 may process and analyze the temperature data locally through a wireless wearable sensor device application 108 stored in the memory 106 and implemented by the processor 104.

The sensor 102 may be one or more thermistors, and the processor 104 is any of a microprocessor and a reusable electronic module. One of ordinary skill in the art will readily recognize that a variety of devices may be used for the sensor 102, processor 104, memory 106, wireless wearable sensor device application 108, and transmitter 110, and such devices would be within the spirit and scope of the present application.

The wireless wearable sensor device 100 may be an ultra-low cost and fully disposable battery-powered adhesive biometric patch biosensor with an integrated sensor/thermistor and Bluetooth Low Energy (BLE) transceiver that is attached to the skin of a user and used in conjunction with an electronic module to detect, record, and analyze multiple temperature data from the human skin surface and the sensor ambient air surrounding the sensor. The wireless wearable sensor device 100 constantly collects temperature data from the patch wearer. The wireless wearable sensor device 100 can then encrypt and send the encrypted data through two-way communication to the hub repeater, which in turn transmits the data to a secure server where it is stored for viewing, downloading, and analysis. With this information, the healthcare provider can observe the patient's temperature improvement or decline in real time and intervene as necessary. To improve the delivery of the bio-sensor events, the events, including live events, are saved to flash memory on the wireless wearable sensor device 100 to avoid data loss. By storing the data locally, the wireless wearable sensor device 100 does not need to maintain a constant bluetooth connection. For example, data is not lost when the wearer is out of bluetooth range, and reconnection occurs automatically when the wireless wearable sensor device 100 is within range of the hub repeater. When the wireless wearable sensor device 100 connects to the repeater, the wireless wearable sensor device 100 will send data at regular intervals and receive an acknowledgement of successful transfer from the repeater. The wireless wearable sensor device 100 may include onboard flash memory that stores firmware, configuration files, and sensor data. The healthcare provider can configure how the sensors collect data. Depending on the manner in which the biosensor is used, a single data stream (e.g., temperature data) may be enabled or disabled.

The temperature data from the body skin surface and the sensor ambient air surrounding the sensor is then processed and analyzed using the integrated processor and algorithm of the wearable device 100 (e.g., reusable electronic module or system-on-a-chip board) or an external processing device (e.g., smartphone device, cloud-based server network).

Further, one of ordinary skill in the art will readily recognize that a variety of wireless wearable sensor devices may be utilized, including but not limited to wearable devices, which would be within the spirit and scope of the present application.

Fig. 2 shows a flow chart 200 for determining a patient temperature (denoted herein as body temp). Thus, at 210, the wearable sensor is used to measure two or more temperature values of the sensor ambient air at the patient's body skin surface and surrounding the sensor. At 220, ambient temperature fluctuations are adaptively eliminated from the skin temperature measurements by using, for example, an adaptive filter. At 230, the core body heat exchange at the patient's body surface is determined by subtracting the ambient filter output from the skin temperature value, also described as heat flux, heat transfer, or core body heat transfer. At 240, the derived absolute magnitude value of the core body heat exchange is controlled and modified by subtracting the heat exchange DC offset and adding a calibration input value that converts the time variable of the core body heat exchange. At 250, the heat exchange of the metric transition is output as the patient temperature. Additional details of the above systems and methods for patient temperature measurement are described by the flow chart representation in fig. 3.

Fig. 3 shows a flow chart 300 of a patient temperature prediction algorithm. The patient temperature assessment begins with the initialization of a stabilization time marker (t) at 301_sFlag) and the initial value of the calibration flag (cal flag) is 0. The wearable sensor device 302 may include two or more temperature transducers, allowing independent direct sampling of the temperature at the skin surface 303 (represented here as skinntemp) and the temperature of the nearby sensor ambient air 304 (represented here as AmbTemp). If multiple transducer networks are used for the SkinTemp measurements, the calculation from the outputs of the temperature transducer networks includes an average or median valueThe appropriate statistical measure for inclusion is called skinntemp. Similarly, one or more separate temperature transducer networks may be employed to determine AmbTemp measurements. Furthermore, the temperature transducers used to measure the skinntemp and AmbTemp may have different resolutions and sampling frequencies, in which case the two measurements may be converted to the same values of higher or lower resolution and sampling frequency, respectively, depending on the performance specifications. Temperature transducers such as thermistors, Resistance Thermometer Detectors (RTDs), and thermocouples, wearable sensor devices such as patch sensors, pendants, wristbands, watches, or body-worn electronic modules are within the scope of the present application. Thus, the wearable sensor system may allow independent direct sampling of skinntemp from the skin surface and AmbTemp from the sensor ambient air using two or more temperature transducer networks.

The input values f (n) of skinntemp 305 are passed through an adaptive filter 306 to produce an output sequence y (n)307, the adaptive filter output. The filter coefficients are updated by minimizing the error 308,

e(n)＝d(n)-y(n) (3)

where { d (n) }309 is the reference input value required for AmbTemp.

Subtracting the adaptive filter output from the input skinnTemp to determine the thermal exchange between the skin surface and the core 310

T_x(n)＝f(n)-y(n) (4)

Time-varying changes in heat exchange between the core body and the skin surface are quantified.

Meanwhile, skinntemp { f (n) } is passed through the differentiator 320, and only when ts _ flag 321 is not currently started (i.e., ts _ flag is 0), the differentiator 320 may determine a unit time difference (i.e.,wherein, T_sIs skinntemp) and the difference between the current skinntemp value and the previous skinntemp value. SkinTempIs calculated as the derivative of and U_THIs a thresholdThe values are compared (e.g., 0.01 is 10% of the skinntemp unit display resolution (e.g., 0.1) (corresponding to a 90% reduction in the rate of change of skinntemp due to the stabilization process.) in further examples, the skinntemp derivative is filtered or averaged over a predetermined time window to compare to a threshold value (e.g., 5%) or other value of the unit display resolutionThe time elapsed to satisfy this condition will be determined as the stabilization time t of the temperature sensor_s325. On the other hand, if at 322Not less than U_THThen the incoming samples of skinntemp will be compared by differentiator 320 to determine if the condition is satisfiedUntil a settling time is reached. Once the settling time is reached by satisfying the above condition, and t is determined_sFor continuous measurements, then at 324 ts _ flag will be set to 1 (i.e., ts _ flag is 1). After ts _ flag starts, the comparison of the processing with the differentiator 320 and the above conditions will stop.

After simultaneously and continuously determining whether the settling time flag is started and estimating heat flux as a measure of local heat exchange, the algorithm now checks the current value of cal _ flag to be 0 or 0 at 330>0 determines whether the calibration flag (i.e., cal _ flag) has been set. If cal _ flag is not present at 331>0 (i.e., the cal _ flag value is still 0), the algorithm checks for a settling time flag start at 332 (ts _ flag)>0? ). If cal _ flag is not started and ts _ flag is set at 333 (ts _ flag)>0) Then a calibration value T will be prompted_cal335 are obtained from the reference device and input into the algorithm at 334 through a suitable user interface. Upon receiving a calibration input T_calAt 336, cal _ flag will start to be 1 (i.e., cal _ flag ═ 1), and the algorithm does not require any further calibration input values for continuously determining the patient body temp 340. In spite of this, it is possible to provide,if the user prompts to feed calibration inputs (recalibration) via the user interface, the algorithm will take into account the latest user inputs for the patient's body temp 340 calculations. On the other hand, if the calibration flag does not start and the settling time flag does not start at 337, the BodyTemp output of the algorithm will be invalidated at 338 until both the settling time and calibration inputs are complete. The invalid code output by the invalid body temp may be a unique value, such as a negative value or a more positive value outside the body temperature range. In one case, if the calibration input is obtained immediately after the biosensor application, the BodyTemp value may simply be output as an input calibration value until the temperature sensor has still stabilized to steady state.

At a stable time t_sAnd receiving a calibration input T_calThereafter, the absolute value of the core body heat exchange will be determined as the stable offset T at the block of the level control 339_soAnd the patient temperature output is calculated using the following equation,

T_so＝T_x(n),n＝t_s*f_s (5)

T_b(n)＝T_x(n)-T_so+T_cal,n≥t_s*f_s (6)

wherein, T_bOutputting for body temperature; t is_soIs the stable offset temperature; t is t_sIs the settling time of the sensor; f. of_sIs the sampling rate of f, skinntemp; t is_calIs the patient temperature input from the reference device. The absolute level of the body temp output 340 is controlled by the user's calibration input 334. If there is no level control 339, the inputs (e.g., calibration values, relative change in hot swap, T)_x) There may not be an absolute measurement comparable to the standard temperature measurement range. In this case, the relative trend of change T of the heat exchange_xRather than associating the temperature change with an absolute scale that can distinguish between normal or abnormal ranges, it may be used to determine the degree of increase or decrease in temperature change that a patient or user experiences from time to time. For the first calibration input T_calFrom which value T of AC offset_soMinus heat exchange T_xAnd will T_calIs added as an input calibration valueDC for determining the body temp output with an absolute measure set by the calibration input. Patient body temp output T_bMay be displayed by communicating with an appropriate display device (e.g., monitor, display, tablet, screen, etc.) or transmitted for storage on local sensor or repeater memory or the cloud. In one example, the construction of wearable device 302 is modeled as, for example, a FIR filter and applied to temperature data from the body skin surface and sensor ambient air surrounding the sensor to provide relatively more accurate adjusted temperature data sampled from sensor 303 of the body skin interface and sensor 304 of the ambient air interface in the wearable device. The corrected measurements of skin and ambient temperature are then used to determine the patient's body temperature in method 300.

The adaptive filter 306 used to determine the patient body temp 340 may be a set of instructions or a program defined by filter types such as linear (including Least Mean Square (LMS), Recursive Least Squares (RLS) filters and variants thereof) or non-linear (including Volterra and bilinear filters) or non-classical (including artificial neural networks, fuzzy logic and genetic algorithms), structural (including lateral, symmetric, lattice and systolic arrays), parametric (including zero, pole and polynomial coefficients), and adaptive algorithms (including random gradient methods and least squares estimation) performed on a microprocessor or digital signal processing chip or field programmable gate array or custom Very Large Scale Integration (VLSI) circuit or system on a chip (SOC)).

RLS adaptive filter

For example, consider a Recursive Least Squares (RLS) adaptive filter with Finite Impulse Response (FIR) coefficients of length M, e.g., b_k(k-0, 1,2, … M-1) for the adaptive filter block 306 of the patient body temp prediction algorithm. The RLS filter can effectively adapt to the time-varying characteristic of input temperature change and has high convergence speed. In one example, an RLS filter implementation for adaptive cancellation from sensor ambient air in skinntemp is given below.

Given the new skinntemp input vector f (n)305 and the desired reference AmbTemp vector d (n)309, the FIR filter outputs y (n) are calculated using the previous set of filter coefficients b (n-1) 307 to,

y(n)＝f^T(n)b(n-1) (7)

wherein the initialized filter coefficient is b (0) ═ 0.

The error is calculated according to equation (3).

Computing a Kalman gain vector of

Where λ is a system memory or forgetting factor that affects the convergence and stability of the filter coefficients and the ability of the filter to track the time-varying characteristics of the input vector; r (n) is an autocorrelation matrix given by,

wherein R is initialized^-1(0) δ I; δ, a regularization parameter, e.g., 0.01; i is an identity matrix.

Inverse correlation matrix R to be used for the next iteration^-1(n) updating the data to be,

R^-1(n)＝λ^-1[R^-1(n-1)-k(n)f^T(n)R^-1(n-1)] (10)

the filter coefficients for the next iteration are updated to,

b(n)＝b(n-1)+k(n)e(n) (11)

for example, the parameters of the RLS filter may be selected as follows: the order M of the filter is 1, the forgetting factor λ is 0.9999, and the regularization factor δ is 0.1.

In the case of system power reset and wearable device reapplication on the user, the adaptive filter parameters include filter coefficient b, error signal e, inverse correlation matrix R^-1The settling time flag ts _ flag, the calibration flag cal _ flag, are initialized to zero and the above process is repeated to provide a continuous body temp output. In the case of conventional biosensor operation and recalibration requests, i.e. when the user prompts through the user interfaceWhen the new calibration value is pushed to the system, the BodyTemp algorithm retains the adaptive filter parameters for adaptive determination of hot-swapping and converts the absolute measure of hot-swapping to a new DC level according to the new calibration (i.e., recalibration) input value. The proposed algorithm and system allows for multiple recalibrations according to the requirements of the user/healthcare provider/clinical administrator. However, in the case of frequent recalibration, the trend of the BodyTemp output needs to be interpreted taking into account a plurality of recalibration timings and input values.

Sample settling time data

The settling time of raw temperature data from a wearable sensor device applied to a patient may vary widely depending on the initial electrical response of the temperature transducer, the type of skin of the patient, the contact pressure, or the adhesion of the sensor on the body surface. During the settling time of the application of the wearable sensor device on the skin surface, the measured raw temperature data from the skin surface and the sensor ambient air may show a drastic change of its absolute value, which is in the range of a few degrees celsius to 10 ℃. Applying the calibration values to shift the derived BodyTemp metric before the raw temperature measurements stabilize may result in an erroneous absolute metric throughout the sensor life cycle unless another recalibration is applied after the stabilization time. Thus, to minimize error, calibration inputs are applied to translate the derived absolute measure of BodyTemp after a settling time of good confidence. Thus, the stabilization time of the sensor, either objectively evaluated or predetermined based on clinical data using raw temperature values according to method 300, is used for an accurate BodyTemp absolute measurement. Fig. 4a shows a trace of the temperature values measured at the chest skin surface after application. In another example, calibration inputs may be obtained during the time the sensor is applied to the patient's body and applied after a customized objective assessment of the settling time of the temperature sensor response for body temp prediction. This approach may be practical from a hospital workflow or other use case point of view, as it is observed that patient temperature may not change drastically in minutes. The rate of change of body temperature over a 24 hour period is a very slow frequency phenomenon when the user/patient is normal during sensor application and calibration. However, if it is determined that the user/patient has a fever during sensor application and calibration, it is advisable to recalibrate after a typical stabilization period or patient temperature reaches a steady state, or to do so, for obtaining a more accurate absolute BodyTemp value. Further, fig. 4a depicts the skinntemp time curves recorded from a group of participants immediately after adhesive sensor application, and fig. 4b depicts the difference in successive skinntemp values as their rate of change, which demonstrates the inherent transient phase of the stabilization process of the temperature sensor output.

In one example, the stabilization time of the temperature sensor may also be preset to a desired stabilization duration, e.g., 30 minutes, based on an analysis of the temperature distribution obtained from the sample population. In this case, the automatic determination that the temperature sensor is stable is replaced by a timer and a check is made as to whether the timer has elapsed the required input settling time.

In another example of objectively determining whether a temperature sensor is stable after its warm-up transient phase involves fitting a linear regression line (L ═ α × d + β, where α is the slope of line L and β is the intercept or offset) with a predetermined moving time window (e.g., 5 minutes) of the SkinTemp derivative samples and determining α (the rate of stability as the slope of the linear fit). The above process is repeated once per predetermined duration (e.g. 1 minute) and the trend of a is followed. The trend of a determined is further used to determine the stationary phase as the time when a approaches zero with some tolerance (e.g. 5% or 10%) or reaches a global minimum at some start-up phase.

Sample predicted BodyTemp data

Sample skinntemp (indicated by legend ST) and AmbTemp (indicated by legend AT) data collected over 3 days are shown in fig. 5 a. The graph shows high fluctuations of 2 ℃ to 6 ℃ in the skinntemp, especially during the transition from night to day. Such high fluctuations in skinntemp do not reflect the 1 c change that typically occurs in the core temperature of a patient during the normal circadian cycle. Thus, the absolute measurement of skinntemp may not be accurate in its direct form without any additional conversion or adjustment. AmbTemp also exhibits similar fluctuations and primarily affects these high fluctuations in skinntemp. Thus, the BodyTemp algorithm adaptively cancels the AmbTemp effect from the SkinTemp to determine the BodyTemp. In FIG. 5a, the BodyTemp output is plotted along with the SkinTemp and AmbTemp from the same sample record for comparison. The predicted body temp shows a very stable trend of fluctuation <1 ℃ over a 3 day duration. Fig. 5b now includes the OralTemp reference value taken during 3 days of data collection in this control object (repeated 3 times). There is a good correspondence between the reference OralTemp and the predicted body temp value.

Calibration input

The BodyTemp algorithm determines T according to equation (4)_x(time-varying heat exchange) which is a continuous measure of the variation in heat exchange between the body and its surroundings. From T after patch stabilization time_xFurther subtracting T from_soIt is possible to substantially remove the DC component of the heat exchange and provide only the AC component or incremental change in the heat exchange. Thus, (T)_x-T_so) Refers to the pure AC component of heat exchange from the upper body, which is itself very useful for clinical monitoring of patient deterioration due to infection or fever. However, this amount may not have an absolute magnitude (measure) similar to the patient temperature (e.g., normal) with a DC value of about 37 ℃. To convert this measure of AC heat exchange to be similar to the clinical patient temperature, quantity (T)_x-T_so) Is added to the DC component T_calThe calibration input as given in equation (6) results in a BodyTemp output T_bAnd (4) predicting. Thus, the temperature T is calibrated_calThe measure of the AC component of the heat exchange is converted into a measure of body temp which is similar to the measure of the body temperature of the patient.

Calibrating temperature T_calThe body temp algorithm may be entered via a suitable User Interface (UI) implemented on a computer, smartphone/device, tablet, etc. For example, a nurse or clinician may measure patient temperature using standard tools (e.g., an oral thermometer or another clinical patient temperature monitor) and manually input via the UI. The BodyTemp prediction algorithm may be modified, wherein the calibration input may be replaced with a large oneA transformation model for clinical research and training in hospitals is suitable for measuring infection and sepsis conditions from a wide range of patient temperatures including fever, infection and sepsis by using a golden standard reference patient temperature through an invasive thermistor probe. Further, the BodyTemp output may be modified to account for systematic offset adjustments as compared to the golden standard temperature reference measurement. In another example, a transformation of the learned SkinTemp error profile may also be used as a substitute for the calibration input.

Those skilled in the art will appreciate that for the processes and methods disclosed herein, as well as other processes and methods, the functions performed in the processes and methods may be performed in a differing order. Further, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, may be combined into fewer steps and operations, or expanded into additional steps and operations without deviating from the essence of the disclosed embodiments.

Furthermore, the present disclosure is not limited to the particular embodiments described in this application, which are intended as illustrations of various aspects. It will be apparent to those skilled in the art that many modifications and variations can be made without departing from the spirit and scope thereof. Functionally equivalent methods and even devices within the scope of the present disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds, compositions, or biological systems, which can, of course, vary. 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.

FIG. 6 illustrates an example computing device 600 in which various embodiments of wearable sensors in a ubiquitous computing environment can be implemented. More specifically, fig. 6 illustrates an illustrative computing embodiment in which any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer readable instructions may be executed by a processor of a mobile unit, a network element, and/or any other computing device, for example.

In a very basic configuration 602, computing device 600 typically includes one or more processors 604 and a system memory 606. A memory bus 608 may be used for communicating between the processor 604 and the system memory 606.

Depending on the desired configuration, processor 604 may be of any type including, but not limited to, a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 604 may include one or more levels of cache, such as a level one cache 610 and a level two cache 612, a processor core 614, and registers 616. Example processor cores 614 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing Core (DSP Core), or any combination thereof. An example memory controller 618 may also be used with processor 604, or in some implementations memory controller 618 may be an internal part of processor 604.

Depending on the desired configuration, the system memory 606 may be of any type including, but not limited to, volatile memory (e.g., RAM), non-volatile memory (e.g., ROM, flash memory, etc.), or any combination thereof. System memory 606 may include an operating system 620, one or more application programs 622, and program data 624.

Applications 622 may include client application 680, which client application 680 is arranged to perform the functions described herein, including those previously described in relation to fig. 1-5. Program data 624 may include a table 650, alternatively referred to as a "digital table 650" or a "distribution table 650," which may be used to determine a patient temperature as described herein.

Computing device 600 may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration 602 and any required devices and interfaces. For example, a bus/interface controller 630 may be used to facilitate communications between basic configuration 602 and one or more data storage devices 632 via a storage interface bus 634. The data storage device 632 may be a removable storage device 636, a non-removable storage device 638, or a combination thereof. Examples of removable and non-removable storage devices include magnetic disk devices, such as floppy disk drives and Hard Disk Drives (HDDs), optical disk drives, such as Compact Disk (CD) drives or Digital Versatile Disk (DVD) drives, Solid State Drives (SSDs), and tape drives, among others. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data.

System memory 606, removable storage 636 and non-removable storage 638 are examples of computer storage media. Computer storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computing device 600. Any such computer storage media may be part of computing device 600.

Computing device 600 may also include an interface bus 640 for facilitating communication from various interface devices (e.g., output devices 642, peripheral interfaces 644, and communication devices 646) to the basic configuration 602 via the bus/interface controller 630. Example output devices 642 may include a graphics processing unit 648 and an audio processing unit 650, which may be configured to communicate with various external devices (e.g., a display or speakers) through one or more A/V ports 652. Example peripheral interfaces 644 can include a serial interface controller 654 or a parallel interface controller 656, which can be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports 458. An example communication device 646 may include a network controller 660, which may be arranged to facilitate communications with one or more computing devices 662 over a network communication link via one or more communication ports 664.

A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. The "modulated data signal" may be a signal that: one or more features of which are set or changed in order to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

Computing device 600 may be implemented as part of a small-form factor portable (or mobile) electronic device such as a cell phone, a Personal Digital Assistant (PDA), a personal media player device, a wireless network watch device, a personal headset device, an application specific device, or a hybrid device that incorporate any of the above functions. Computing device 400 may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations.

There is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is often (but not always, since in some cases the choice between hardware and software may become important) a design choice representing cost versus efficiency tradeoffs. There are various vehicles by which processes and/or systems and/or other technologies described herein can be effected, such as hardware, software, and/or firmware, and that the preferred vehicle will vary with the process and/or context in which the system and/or other technologies are deployed. For example, if the implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware implementation; if flexibility is paramount, the implementer may opt to have a mainly software implementation; or, again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.

The foregoing detailed description has set forth various embodiments of devices and/or processes for determining a body temperature of a patient using block diagrams, flowcharts, and/or examples. With respect to such block diagrams, flowcharts, and/or examples containing one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, portions of the subject matter described herein may be implemented by an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), or other integrated format. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more) one or more computer systems, as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and/or firmware would be well within the skill of one of skill in the art in light of this disclosure. Moreover, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing media used to actually carry out the distribution. Examples of signal bearing media include, but are not limited to, the following: recordable type media such as floppy disks, hard disk drives, CDs, DVDs, digital tape, computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

Those skilled in the art will recognize that it is common within the art to describe devices and/or processes in the manner set forth herein, and then use engineering practices to integrate such described devices and/or processes into a data processing system. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system with a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally includes a system unit housing, a video display device, memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computing entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interactive devices such as touch pads or screens, and/or a control system including feedback loops and control motors (e.g., control motors for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented using any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.

The subject matter described herein sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that this described architecture is merely an example, and that in fact many other architectures can be implemented which achieve the same functionality. Conceptually, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected," or "operably coupled," to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably connected," or "operably coupled," to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting and/or logically interactable components.

Finally, 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 and/or application depending on the context. Various singular/plural permutations may be expressly set forth herein for the sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims, such as the 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 will 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 words such as "a" or "an," e.g., "a" and/or "an" should be interpreted to mean "at least one" or "one or more"; the same holds true for the use of definite articles used to introduce claims. Furthermore, even if specific numbers are recited in the claims incorporated herein by reference, those skilled in the art will recognize that such reference should be construed as a bare reference to at least the recited number, e.g., "two references," and not as an additional modifier, meaning at least two references, or two or more references. Further, used in the context of those conventions similar to "A, B and at least one of C, etc." in general, this configuration is intended in the sense that those skilled in the art will understand the convention, e.g., "a system having at least one of A, B and C" would include, but not be limited to, the following systems: having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B and C together, and the like. In general, this configuration is intended to be in the sense that those skilled in the art will understand convention, for example, "a system having at least one of A, B or C" will include, but is not limited to, the following systems: having a alone a, B alone, C, A alone and B together, a and C together, B and C together, and/or A, B and C together, and so forth. 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 as contemplating possibility of including one, either, or both terms. For example, the phrase "a or B" will be understood to include the possibility of "a" or "B" or "a and B".

Further, where features or aspects of the disclosure are described in terms of markush groups, those skilled in the art will recognize that the disclosure is thereby also described in terms of any individual member or subgroup of members of the markush group.

From the foregoing, it will be appreciated that various embodiments of the disclosure have been described herein for purposes of illustration, and that various modifications may be made without deviating from the scope and spirit of the disclosure. Therefore, the various embodiments disclosed herein are not intended to be limiting, with the true scope and spirit being indicated by the following claims.