Calibration of a wearable medical device

• US 10,314,547 B2
• Filed: 04/09/2014
• Issued: 06/11/2019
• Est. Priority Date: 04/09/2014
• Status: Active Grant

• Current Assignee: HALO Wearables LLC
• Original Assignee: HALO Wearables LLC
• Inventors: Miller, Devin Warner, Miller, David Rich, Lee, Jeffrey Michael
• Primary Examiner(s): Rivera Vargas, Manuel A
• Application Number:
  US14/248,334
• Publication Number:
  US 20150289820A1
• Time in Patent Office: 1,889 Days
• CPC Class Codes: A61B 2560/0238 Means for recording calibration data
• CPC Class Codes: A61B 2560/0252 using ambient temperature
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/0535 Impedance plethysmography for measuring blood flow A61B5/0295
• CPC Class Codes: A61B 5/0536 Impedance imaging, e.g. by tomography
• CPC Class Codes: A61B 5/0537 Measuring body composition by impedance, e.g. tissue hydration or fat content
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/14517 for sweat
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14535 for measuring haematocrit
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/6807 Footwear
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6829 : Foot or ankle
• CPC Class Codes: A61B 5/684 : Indicating the position of the sensor on the body
• CPC Class Codes: A61B 5/7207 : of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7221 : Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7296 : for compensation of signal variation due to stress unintentionally induced in the patient, e.g. due to the stress of the medical environment or examination deliberately inducing physiological or psychological stress A61B5/4884

Abstract

A technology for a wearable medical device for monitoring medical parameters. Medical measurement data can be received at the wearable medical device from a medical measurement sensor attached to the wearable medical device or a medical measurement sensor in communication with the wearable medical device. A calibration coefficient can be determined for calibrating the wearable medical device based on the medical measurement data. The wearable medical device can be calibrated based on the calibration coefficient.

Claims

1. A wearable device, comprising:

a first sensor to measure a first physiological characteristic of a user to obtain a first set of physiological data;

a second sensor to measure a second physiological characteristic of the user to obtain a second set of physiological data, wherein the second sensor is different than the first sensor; and

a processing device to;

identify a first data point in the first set of physiological data that is orthogonal to a second data point in the second set of physiological data; and

determine a calibration coefficient for calibrating the first sensor or a measurement from the first sensor based on the first data point and the second data point;

determine an accuracy level of the first set of physiological data; and

in response to the accuracy level of the first set of physiological data being below a threshold level, calibrate the first sensor or the measurement from the first sensor based on the calibration coefficient.

2. The wearable device of claim 1, wherein to determine the calibration coefficient and calibrate the first sensor or the measurement from the first sensor, the processing device is further to:

identify a trend of a physiological characteristic of the user;

determining the calibration coefficient based on the trend; and

calibrating the first sensor or the measurement from the first sensor based on the calibration coefficient.

3. The wearable device of claim 2, whether in the trend is indicative of a threshold weight gain by the user or a threshold weight loss by the user.

4. The wearable device of claim 3, wherein the processing device is to:

receive weight data of the user from another device;

determine a first weight of the user at a first point in time;

determine a second weight of the user at a second point in time;

determine a third weight of the user at a third point in time; and

determine the trend based on the first weight of the user, the second weight of the user, and the third weight of the user.

5. The wearable device of claim 2, wherein the trend is a trend in the first set of physiological data.

6. The wearable device of claim 1,

wherein the processing device is to perform multivariate analysis on the first set of physiological data and the second set of physiological data to determine the calibration coefficient, wherein:

the multivariate analysis is an analysis of more than one variable of the first set of the physiological data and the second set of physiological data; and

the multivariate analysis is an analysis of the more than one variable of the first set of the physiological data and the second set of physiological data across multiple dimensions to account the more than one variable.

7. The wearable device of claim 1, further comprising a third sensor to measure an environmental characteristic of an area approximate to the user to obtain a first set of environmental data, wherein the processing device is to:

determine an environmental interference on the first set of physiological data based on the first set of environmental data;

determine the calibration coefficient indicating a change is an accuracy of the first set of physiological data in view of the environmental interference; and

calibrate the first sensor or the measurement from the first sensor based on the calibration coefficient.

8. The wearable device of claim 1, further comprising a communication device configured to receive medical data from an external device, wherein the processing device is to:

determine a medical condition of the user indicated in the medical data; and

calibrate the first sensor or the measurement from the first sensor in view of the medical condition of the user.

9. The wearable device of claim 1, wherein to determine the calibration coefficient, the processing device is further to:

determine a health status of the user; and

determine the calibration coefficient based on the health status of the user.

10. The wearable device of claim 1, wherein to determine the calibration coefficient, the processing device is further to:

receive, from another device, a second set of physiological data for a group of individuals;

generate a histogram based on the second set of physiological data; and

determine the calibration coefficient based on the histogram and the first set of physiological data.

11. The wearable device of claim 1, wherein to determine the calibration coefficient, the processing device is further to:

receive, from another device, medical data indicating a medical condition of the user; and

determine the calibration coefficient based on the first set of physiological data and the medical condition of the user.

12. The wearable device of claim 1, wherein to determine the calibration coefficient and calibrate the first sensor or the measurement from the first sensor, the processing device is further to:

receive, from another device, medical data indicating a medical condition of the user; and

identify the calibration coefficient for another user with the same medical condition; and

calibrating the first sensor or a measurement from the first sensor using the calibration coefficient for the other user.

13. The wearable device of claim 1, wherein to determine the calibration coefficient and, the processing device is further to:

determine whether the first set of physiological data is at least a standard deviation of from a second set of physiological data;

determine the calibration coefficient to decrease a difference between the first set of physiological data and the second set of physiological data to be less than the standard deviation; and

calibrating the first sensor or the measurement from the first sensor based on the calibration coefficient.

14. The wearable device of claim 1, wherein to determine the calibration coefficient and calibrate the first sensor or the measurement from the first sensor, the processing device is further to:

identify a pattern of a physiological characteristic of the user in the first set of physiological data;

determining the calibration coefficient based on the pattern; and

calibrating the first sensor or the measurement from the first sensor based on the calibration coefficient.

15. A non-transitory computer-readable storage medium comprising instructions configured to cause a computing device to perform operations, the operations comprising:

receiving, from a first sensor, a first physiological characteristic of a user to obtain a first set of physiological data, the first sensor being physically coupled to a wearable device;

receiving, from a second sensor, a second physiological characteristic of the user to obtain a second set of physiological data, the second sensor being physically coupled to the wearable device;

identifying a first data point in the first set of physiological data that is orthogonal to a second data point in the second set of physiological data;

determining a unique physiological characteristic of the user based on the first set of physiological data;

determining a calibration coefficient the sensor or measurements from the sensor for the unique physiological characteristic in view of the first data point and the second data point; and

calibrating the sensor or the measurement from the sensor based on the calibration coefficient.

16. The computer-readable storage medium of claim 15, wherein the unique physiological characteristic of the user is a physiological characteristic that is different than a physiological characteristic of another user of the wearable device.

17. The computer-readable storage medium of claim 15, wherein the unique physiological characteristic of the user is a physiological characteristic common to a defined group of individuals.

18. A method, comprising:

measuring, by a first sensor, a first physiological characteristic of a user to obtain a first set of physiological data;

measuring, by a second sensor, a second physiological characteristic of the user to obtain a second set of physiological data;

identifying, by a processor, a first data point in first set of physiological data that is orthogonal to a second data point in the second set of physiological data;

determining, by the processor, a first calibration coefficient for calibrating the first sensor or the measurement from the first sensor based on the first data point and the second data point; and

calibrating, by the processor, the first sensor or the measurement from the first sensor based on the determined first calibration coefficient.

19. The method of claim 18, wherein calibrating the first sensor or the measurement from the first sensor further comprises:

determining, by the processor, a first error rate of the first set of physiological data;

calibrating, by the processor, a wearable device based on the first calibration coefficient to reduce the first error rate;

measuring, by the first sensor, the first physiological characteristic of the user to obtain a second set of physiological data;

determining, by the processor, a second error rate of the second set of physiological data;

determining, by the processor, whether the second set of physiological data is in a stable state;

when the second set of physiological data is in the stable state where the second error rate is below a threshold error rate level, calibrating, by the processor, the wearable device based on the first calibration coefficient; and

when the second set of physiological data is not in the stable state where the second error rate exceeds the threshold error rate level;

determining a second calibration coefficient to reduce the second error rate below the threshold error rate level; and

calibrating, by the processor, the wearable device based on the second calibration coefficient.