MULTIMODE SENSOR DEVICES

• US 20140378872A1
• Filed: 09/09/2014
• Published: 12/25/2014
• 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
• Granted Patent: US 10,216,894 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• 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 method of tracking a user'"'"'s physiological activity using a worn biometric monitoring device having one or more sensors providing output data indicative of the user'"'"'s physiological activity, the method comprising:

(a) analyzing a sensor output data provided by the worn biometric monitoring device to determine that the user is engaged in a first activity that produces a relatively high signal-to-noise ratio (SNR) in the sensor output data;

(b) quantifying a physiological metric by analyzing a first set of sensor output data in a time domain;

(c) analyzing a subsequent sensor output data provided by the worn biometric monitoring device to determine that the user is engaged in a second activity that produces a relatively low SNR in the subsequent sensor output data; and

(d) quantifying the physiological metric from a periodic component of a second set of sensor output data by processing the second set of sensor output data using a frequency domain analysis,

wherein the quantifying in (d) requires more computation per unit of the physiological metric than the quantifying in (b).

2. The method 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.

3. 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.

4. The method of claim 1, wherein analyzing the sensor output data in (a) or analyzing the subsequent sensor output data in (c) 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.

5. The method of claim 1, further comprising analyzing a biometric information previously stored on the worn biometric monitoring device to determine that the user is engaged in the first or the second activity.

6. The method of claim 1, wherein the first set of sensor output data comprises data from only one axis of a multi-axis motion sensor, and wherein the second set of sensor output data comprises data from two or more axis of the multi-axis motion sensor.

7. The method of claim 1, wherein the one or more sensors comprise a motion sensor, and wherein analyzing the sensor output data in (a) or analyzing the subsequent sensor output data in (c) comprises using a motion signal to determine whether the user is engaged in the first activity or the second activity.

8. The method of claim 7, wherein the first activity comprises a free motion of a limb wearing the worn biometric monitoring device during the first activity.

9. The method of claim 7, wherein the second activity comprises a reduced motion of the limb wearing the worn biometric monitoring device during the second activity.

10. The method of claim 9, wherein the second activity involves the user holding a substantially non-accelerating object with the limb wearing the worn biometric monitoring device.

11. The method of claim 1, wherein the analyzing in (b) comprises peak detection of a signal in the first set of sensor output data.

12. The method 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.

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

14. The method of claim 1, wherein the frequency domain analysis comprises performing a Fisher'"'"'s periodicity test.

15. The method of claim 1, wherein the frequency domain analysis comprises using a harmonic to estimate a period and/or a test periodicity.

16. The method 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.

17. The method of claim 1, wherein (b) and (d) each comprise:

identifying a periodic component from the first or second sensor output data; and

calculating the physiological metric from the periodic component from the first or second set of sensor output data.

18. The method of claim 1, wherein the physiological metric comprises a step count.

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

(a) analyzing a sensor output data to characterize the sensor output data as indicative of a first activity associated with a relatively high signal level or indicative of a second activity associated with a relatively low signal level;

(b) processing the sensor output data indicative of the first activity to produce a first value of a physiological metric; and

(c) processing the sensor output data indicative of the second activity to produce a second value of the physiological metric,

wherein the processing in (b) requires more computation per unit of the physiological metric than the processing in (c).

20. A biometric monitoring device comprising:

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

a control logic configured for;

(a) analyzing a sensor output data provided by the biometric monitoring device to determine that the user is engaged in a first activity that produces a relatively high SNR in the sensor output data;

(b) quantifying a physiological metric by analyzing a first set of sensor output data in a time domain;

(c) analyzing a subsequent sensor output data provided by the biometric monitoring device to determine that the user is engaged in a second activity that produces a relatively low SNR in the subsequent sensor output data; and

(d) quantifying the physiological metric from a periodic component of a second set of sensor output data by processing the second set of sensor output data using a frequency domain analysis,

wherein the quantifying in (d) requires more computation per unit of the physiological metric than the quantifying in (b).

21. The biometric monitoring device of claim 20, wherein the biometric monitoring device comprises a wrist-worn biometric monitoring device or an arm-worn biometric monitoring device.

22. The biometric monitoring device of claim 20, wherein analyzing the sensor output data in (a) or analyzing the subsequent sensor output data in (c) 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.

23. A biometric monitoring device comprising:

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

a control logic configured to;

(a) analyze a sensor output data to characterize the sensor output data as indicative of a first activity associated with a relatively high signal level or indicative of a second activity associated with a relatively low signal level;

(b) process the sensor output data indicative of the first activity to produce a first value of a physiological metric; and

(c) process the sensor output data indicative of the second activity to produce a second value of the physiological metric.

wherein the processing in (b) requires more computation per unit of the physiological metric than the processing in (c).