Multimode sensor devices

• US 10,216,894 B2
• Filed: 09/09/2014
• Issued: 02/26/2019
• Est. Priority Date: 09/30/2010
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Hong, Jung Ook, Axley, Andrew Cole, Yuen, Shelten Gee Jao
• Primary Examiner(s): Cerioni, Daniel L
• Application Number:
  US14/481,020
• Publication Number:
  US 20140378872A1
• Time in Patent Office: 1,631 Days
• Field of Search: 600587, 600595
• 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 and the BMD applies a time domain analysis on data provided by a sensor of the BMD when the data has a high signal (e.g., high signal-to-noise ratio), and applies a frequency domain analysis on the data when the data has a low signal, which contributes to improved accuracy and speed of biometric data.

Claims

1. A biometric monitoring device comprising:

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

a display device configured to display values of physiological metrics generated for the user; and

one or more processors configured to;

(a) operate the one or more sensors to provide the sensor output data when the biometric monitoring device is worn by the user;

(b) determine that a first portion of the sensor output data includes data indicative of the user being engaged in a first activity and that a signal strength characteristic for the first portion of the sensor output data is higher than a first threshold value, wherein the first activity is walking and/or running;

(c) update, based at least in part on the determination of (b), a heart rate metric using information obtained from a time domain analysis of the first portion of the sensor output data;

(d) determine that a second portion of the sensor output data includes data indicative of the user being engaged in a second activity and that a signal strength characteristic for the second portion of the sensor output data is less than or equal to a second threshold value, wherein the second activity is selected from the group consisting of;

elliptical machine exercise, stair machine exercise, cardio exercise machines, weight training, driving, swimming, biking, sleeping, driving, stair climbing, rock climbing, and any combination thereof;

(e) update, based at least in part on the determination of (d), the heart rate metric using information obtained from a frequency domain analysis of the second portion of the sensor output data;

(f) repeat (b) through (e) for additional portions of the sensor output data; and

(g) control the display device of the biometric monitoring device to display the heart rate metric.

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

3. The biometric monitoring device of claim 1, wherein operation (b) comprises characterizing the sensor output data based on a signal norm, a signal energy/power in certain frequency bands, a wavelet scale parameter, and/or a number of samples exceeding one or more thresholds.

4. The biometric monitoring device of claim 1, wherein the frequency domain analysis in (e) requires more computation per unit of duration of the sensor output data than the time domain analysis in (c).

5. The biometric monitoring device of claim 1, wherein the frequency domain analysis in (e) requires more computation per unit of the heart rate metric than the time domain analysis in (c).

6. The biometric monitoring device of claim 1, wherein the sensor output data comprise raw data directly obtained from the one or more sensors without preprocessing and/or data derived from the raw data after preprocessing.

7. The biometric monitoring device of claim 1, wherein the one or more processors are further configured to analyze a biometric information previously stored on the biometric monitoring device to determine that the user is engaged in the first or the second activity.

8. The biometric monitoring device of claim 1, wherein the time domain analysis comprises peak detection of a signal in the first portion of the sensor output data.

9. The biometric monitoring device of claim 1, 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.

10. The biometric monitoring device of claim 1, 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.

11. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises finding any spectral peak/peaks that is/are a function of an average step rate.

12. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises performing a Fisher'"'"'s periodicity test.

13. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises using a harmonic to estimate a period and/or a test periodicity.

14. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises performing a generalized likelihood ratio test whose parametric models incorporate a harmonicity of a motion signal.

15. The biometric monitoring device of claim 1, wherein the time domain analysis and the frequency domain analysis each comprise:

identifying a periodic component from the sensor output data; and

calculating the heart rate metric from the periodic component.

16. The biometric monitoring device of claim 1, wherein the first activity produces a signal to noise ratio (SNR) in the first portion of the sensor output data larger than a first threshold, and the second activity produces a SNR in the second portion of the sensor output data equal to or smaller than a second threshold.

17. The biometric monitoring device of claim 16, wherein the first threshold is equal to or greater than the second threshold.

18. A biometric monitoring device comprising:

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

a display device configured to present display values of heart rate metrics generated for the user, and

one or more processors configured to;

(a) operate the one or more heart rate sensors to provide the heart rate data when the biometric monitoring device is worn by the user;

(b) determine that a signal strength characteristic for a first portion of the heart rate data is higher than a first threshold value;

(c) update, based at least in part on the determination that the signal strength characteristic for the first portion of the heart rate data is higher than the first threshold value, a heart rate metric using information obtained from a time domain analysis of the first portion of the heart rate data;

(d) determine that the signal strength characteristic for a second portion of the heart rate data is less than or equal to a second threshold value;

(e) update, based at least in part on the determination that the signal strength characteristic for the second portion of the heart rate data is less than or equal to a second threshold value, the heart rate metric using information obtained from a frequency domain analysis of the second portion of the heart rate data;

(f) repeat (b) through (e) for additional portions of the heart rate data; and

(g) control the display device of the biometric monitoring device to display the heart rate metric.

19. The biometric monitoring device of claim 18, wherein signal strength characteristics are determined by signal-to-noise ratios, signal norms, signal energy or power in certain frequency bands, wavelet scale parameters, and/or a number of samples exceeding one or more thresholds.

20. The biometric monitoring device of claim 18, wherein the first threshold is greater than the second threshold.

21. The biometric monitoring device of claim 18, wherein the first threshold is equal to the second threshold.