Multimode sensor devices

• US 10,381,109 B2
• Filed: 04/10/2014
• Issued: 08/13/2019
• 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:
  US14/250,256
• Publication Number:
  US 20140278139A1
• Time in Patent Office: 1,951 Days
• Field of Search: 33511, 33512, 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 or 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 motion sensors providing motion data;

a display device configured to present step count metrics; and

one or more processors configured to;

operate the one or more motion sensors when the biometric monitoring device is in motion;

control the one or more motion sensors to generate a set of motion data indicative of movements of a user;

apply a frequency domain analysis to the set of motion data to obtain a first measure of a periodic component of the set of motion data;

apply a time domain analysis to the set of motion data to obtain a second measure of the periodic component of the set of motion data;

determine that a signal power of the set of motion data is lower than a threshold;

select, based on determining that the signal power is lower than the threshold, the first measure of the periodic component over the second measure of the periodic component;

update a step count metric of the user using the first measure of the periodic component; and

control the display device of the biometric monitoring device to display the updated step count metric.

2. The biometric monitoring device of claim 1, wherein the one or more sensors comprise one or more motion sensors providing motion intensity data.

3. The biometric monitoring device of claim 2, wherein the one or more motion sensors comprise one or more accelerometers, gyroscopes, inertial sensors, and/or GPS sensors.

4. 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.

5. The biometric monitoring device of claim 1, wherein the signal power of the set of sensor output data is lower than the threshold when the user is active but the user'"'"'s limb wearing the biometric monitoring device is not moving freely.

6. The biometric monitoring device of claim 1, wherein the sensor output data comprise raw data directly obtained from the sensor.

7. 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.

8. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises filtering a time domain signal with a frequency band pass filter.

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

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

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

12. 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.

13. 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.

14. The biometric monitoring device of claim 1, wherein the set of sensor output data comprise motion data, and the one or more sensors are further configured to analyze the motion data of the set of sensor output data to classify motion signals into two categories:

signals generated from steps and signals generated from activities other than steps.

15. The biometric monitoring device of claim 1, wherein the physiological metric comprises a heart rate.

16. The biometric monitoring device of claim 1, wherein the physiological metric comprises stairs climbed, calories burned, and/or sleep quality.

17. The biometric monitoring device of claim 1, further comprising applying a classifier to the set of sensor output data to determine a placement of the biometric monitoring device on the user.

18. The biometric monitoring device of claim 17, wherein updating the physiological metric comprises using information regarding the placement of the biometric monitoring device to determine a value of the physiological metric.

19. The biometric monitoring device of claim 1, wherein the physiological metric comprises a step count.

20. The biometric monitoring device of claim 1, wherein the device maintains an overall accuracy of the biometric monitoring device in measuring the physiological metric.

21. The biometric monitoring device of claim 1, wherein the time domain analysis comprises counting peaks of a time domain representation of the set of sensor output data.