Multimode sensor devices

• US 10,216,893 B2
• Filed: 03/17/2014
• 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:
  US14/216,743
• Publication Number:
  US 20140303523A1
• Time in Patent Office: 1,807 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 motion sensors providing sensor output data comprising information about a user'"'"'s motion;

a display device configured to display a step count metric generated for the user; and

one or more processors configured to;

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

(b) obtain a first set of motion sensor output data generated by the one or more motion sensors indicative of the motion of the biometric monitoring device;

(c) determine that a first signal strength of the first set of motion sensor output data is larger than a threshold value;

(d) select, based on determining that the first signal strength of the first set of motion sensor output data is larger than the threshold value, a time domain analysis of the first set of motion sensor output data over a frequency domain analysis of the first set of motion sensor output data;

(e) quantify a step count using the time domain analysis of the first set of motion sensor output data, and update the step count metric using the step count quantified using the time domain analysis of the first set of motion sensor output data;

(f) obtain a second set of motion sensor output data generated by the one or more motion sensors indicative of the motion of the biometric monitoring device;

(g) determine that a second signal strength of the second set of motion sensor output data is smaller than the threshold value;

(h) select, based on determining that the second signal strength of the second set of motion sensor output data is smaller than the threshold value, the frequency domain analysis of the second set of motion sensor output data over the time domain analysis of the second set of motion sensor output data; and

(i) quantify a step count using the frequency domain analysis of the second set of motion sensor output data, and update the step count metric using the step count quantified using the frequency domain analysis of the second set of motion sensor output data;

(j) control the display device of the biometric monitoring device to display the step count metric.

2. The biometric monitoring device of claim 1, wherein the frequency domain analysis requires more computation per unit of duration of motion sensor output data than the time domain analysis.

3. The biometric monitoring device of claim 1, wherein the frequency domain analysis requires more computation per unit of the step count metric than the time domain analysis.

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

5. 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.

6. The biometric monitoring device of claim 1, wherein the first signal strength is a signal power or a signal-to-noise ratio of the first set of motion sensor output data.

7. The biometric monitoring device of claim 1, wherein the second signal strength is a signal power or a signal-to-noise ratio of the second set of motion sensor output data.

8. The biometric monitoring device of claim 1, wherein the first set of motion sensor output data comprises data from only one axis of a multi-axis motion sensor, and wherein the second set of motion sensor output data comprises data from two or more axes of the multi-axis motion sensor.

9. The biometric monitoring device of claim 1, wherein the first set of motion sensor output data is caused by a free motion of a limb wearing the biometric monitoring device.

10. The biometric monitoring device of claim 1, wherein the second set of motion sensor output data is caused by a reduced motion of a limb wearing the biometric monitoring device.

11. The biometric monitoring device of claim 10, wherein the reduced motion of the limb is produced when the user is holding a steady object with the limb wearing the biometric monitoring device.

12. 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.

13. 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.

14. 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.

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

16. 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.

17. 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.

18. The biometric monitoring device of claim 1,

wherein the time domain analysis comprises:

identifying a periodic component from the first set of motion sensor output data; and

quantifying a step count from the periodic component from the first set of motion sensor output data;

and wherein the frequency domain analysis comprises;

identifying a periodic component from the second set of motion sensor output data; and

quantifying a step count from the periodic component from the second set of motion sensor output data.

19. The biometric monitoring device of claim 1, wherein updating the step count metric in (i) comprises combining the step count quantified using the time domain analysis and the step count quantified using the frequency domain analysis.

20. The biometric monitoring device of claim 1, wherein the frequency domain analysis comprises:

detecting a frequency component from motion sensor output data;

determining a time duration of the motion sensor output data; and

quantifying a step count based on the detected frequency component and the determined time duration.