MULTIMODE SENSOR DEVICES

• US 20140378786A1
• Filed: 09/09/2014
• Published: 12/25/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Hong, Jung Ook, Axley, Andrew Cole, Yuen, Shelten Gee Jao
• Application Number:
  US14/481,762
• Publication Number:
  US 20140378786A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G01C 22/006 : Pedometers
• CPC Class Codes: G16B 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type. The device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process or the BMD determines that the user is engaged in a particular activity type by detecting a signature signal selectively associated with the particular activity type, and quantifies a physiological metric for the particular activity type.

Claims

1. A method of tracking a user'"'"'s physiological activity using a worn biometric monitoring device having one or more sensors providing sensor output data indicative of the user'"'"'s physiological activity, the method comprising:

(a) determining that the user is engaged in a first type of activity by detecting a first signature signal in a sensor output data, the first signature signal being selectively associated with the first type of activity;

(b) quantifying a first physiological metric for the first type of activity from a first set of sensor output data;

(c) determining that the user is engaged in a second type of activity by detecting a second signature signal in a subsequent sensor output data, the second signature signal being selectively associated with the second type of activity and different from the first signature signal; and

(d) quantifying a second physiological metric for the second type of activity from a second set of sensor output data,

wherein the first type of activity differs from the second type of activity.

2. The method of claim 1, wherein the sensor output data comprises one or more of the following:

motion data, location data, pressure data, light intensity data, and/or altitude data.

3. The method of claim 1, wherein the first type of activity and the second type of activity comprise two different activities selected from the group consisting of:

running, walking, elliptical machine exercise, stair master exercise, cardio exercise machines, weight training, driving, swimming, biking, stair climbing, and rock climbing.

4. The method of claim 1, wherein the one or more sensors comprise one or more motion sensors providing motion intensity data.

5. The method of claim 4, wherein the one or more motion sensors comprise one or more accelerometers, gyroscopes, inertial sensors, and/or GPSs.

6. The method of claim 1, wherein the worn biometric monitoring device comprises a wrist-worn biometric monitoring device or an arm-worn biometric monitoring device.

7. The method of claim 1, wherein detecting the first signature signal in the sensor output data comprises characterizing the sensor output data based on a signal norm, signal energy/power in certain frequency bands, a wavelet scale parameter, and/or a number of samples exceeding one or more thresholds.

8. The method of claim 1, wherein detecting the second signature signal in the sensor output data comprises characterizing the sensor output data based on a signal norm, signal energy/power in certain frequency bands, a wavelet scale parameter, and/or a number of samples exceeding one or more thresholds.

9. The method of claim 1, wherein the sensor output data comprise raw data directly obtained from the sensor.

10. The method of claim 1, further comprising:

analyzing the first or the second set of sensor output data to determine that the first or second set of sensor output data has a relatively low signal-to-noise ratio (SNR) while the user is active;

applying a frequency domain analysis to the first or second set of sensor output data to process and/or identify a periodic component; and

determining a physiological metric of the user from the periodic component.

11. The method of claim 10, wherein the frequency domain analysis comprises:

a Fourier transform, a cepstral transform, a wavelet transform, a filterbank analysis, a power spectral density analysis and/or a periodogram analysis.

12. The method of claim 10, wherein the frequency domain analysis comprises filtering a time domain signal with a frequency band pass filter, and then applying a peak detection analysis in the time domain.

13. The method of claim 10, wherein the frequency domain analysis comprises finding a spectral peak that is a function of an average step rate.

14. The method of claim 10, wherein the frequency domain analysis comprises finding spectral peaks that are a function of an average step rate.

15. The method of claim 10, wherein the frequency domain analysis comprises performing a Fisher'"'"'s periodicity test.

16. The method of claim 10, wherein the frequency domain analysis comprises using a harmonic to estimate a period and/or a test periodicity.

17. The method of claim 10, wherein the frequency domain analysis comprises performing a generalized likelihood ratio test whose parametric models incorporate a harmonicity of a motion signal.

18. The method of claim 1, wherein the physiological metric comprises a heart rate.

19. The method of claim 1, wherein the first or second physiological metric comprises stairs climbed, calories burned, and/or sleep quality.

20. The method of claim 1, further comprising applying a classifier to the sensor output data or the subsequent sensor output data to determine the placement of the biometric monitoring device on the user.

21. The method of claim 20, wherein quantifying a first physiological metric or quantifying a second physiological metric comprises using information regarding the placement of the biometric monitoring device to determine a value of the physiological metric.

22. A biometric monitoring device comprising:

one or more sensors providing sensor output data comprising information about a user'"'"'s activity level when the biometric monitoring device is worn by the user;

control logic configured to;

(a) determine that the user is engaged in a first type of activity by detecting a first signature signal in a sensor output data, the first signature signal being selectively associated with the first type of activity;

(b) quantify a first physiological metric for the first type of activity from a first set of sensor output data;

(c) determine that the user is engaged in a second type of activity by detecting a second signature signal in a subsequent sensor output data, the second signature signal being selectively associated with the second type of activity and different from the first signature signal; and

(d) quantify a second physiological metric for the second type of activity from a second set of sensor output data,

wherein the first type of activity differs from the second type of activity.

23. The biometric monitoring device of claim 22, wherein the sensor output data and the subsequent sensor output data comprise motion data.

24. The biometric monitoring device of claim 22, wherein the sensor output data and the subsequent sensor output data further comprise:

location data, pressure data, light intensity data, and/or altitude data.

25. The biometric monitoring device of claim 22, wherein the biometric monitoring device comprises a wrist-worn biometric monitoring device or an arm-worn biometric monitoring device.