IP Library Granted Patent US 10,216,893
Granted Patent B2
US 10,216,893 · App. 14/216,743 · Granted Feb 26, 2019

Multimode sensor devices

Inventors: Jung Ook Hong (Emeryville, CA); Andrew Cole Axley (Oakland, CA); Shelten Gee Jao Yuen (Berkeley, CA)
Assignee: Fitbit, Inc.
G06F19/10A61B5/0205A61B5/112A61B5/1112A61B5/1118A61B5/1121A61B5/1123A61B5/4815A61B5/4866A61B5/681A61B5/6824A61B5/721A61B5/725A61B5/726A61B5/7207A61B5/7235A61B5/7257A61B5/7264A61B5/7282G01C22/006A61B5/024A61B5/7221A61B5/7253G06F19/3481
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Quick Facts
Patent No.
US 10,216,893
App. No.
14/216,743
Granted
Feb 26, 2019
Kind
B2
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 (42)

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.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 10, 2014
From: HONG, JUNG OOK; AXLEY, ANDREW COLE; YUEN, SHELTEN GEE JAO
To: FITBIT, INC.
Reel/Frame 032650/0947 →
Continuity (5)
Continuation In Part 13156304 · Jun 8, 2011
Provisional Application 61800095 · Mar 15, 2013
Provisional Application 61388595 · Sep 30, 2010
Provisional Application 61390811 · Oct 7, 2010
Related Publication 20140303523A1 · Oct 9, 2014
Cited By (2)
US 12,471,790 US 12,564,329