FEATURE EXTRACTION AND CLASSIFICATION TO DETERMINE ONE OR MORE ACTIVITIES FROM SENSED MOTION SIGNALS

• US 20140278208A1
• Filed: 03/16/2014
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: AliphCom, Inc. (AliphCom Corp.)
• Inventors: Donaldson, Thomas Alan
• Application Number:
  US14/215,038
• Publication Number:
  US 20140278208A1
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments of the invention relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and wearable computing devices for facilitating health and wellness-related information. More specifically, disclosed are systems, methods, devices, computer readable medium, and apparatuses configured to determine activity and activity types, including gestures, from sensed motion signals using, for example, a wearable device (or carried device) and one or more motion sensors. In at least some embodiments, an apparatus can include a wearable housing, and a motion sensor configured to generate a motion sensor signal. The apparatus also may include a motion processor configured to generate intermediate motion signals from the motion sensor signal, and an activity processor configured to identify an activity based on the intermediate motion signals.

Claims

1. A method comprising:

receiving data representing a motion sensor signal from a motion sensor disposed in a housing of a wearable device;

generating one or more decomposed signals as intermediate motion signals based on the motion sensor signal;

extracting features of a decomposed signal to form extracted features; and

identifying an activity based on a subset of the extracted features.

2. The method of claim 1, wherein extracting the features of the decomposed signal comprises:

separating the decomposed signal from the motion sensor signal; and

forming one or more subsets of the extracted features, each of the subsets of the extracted features including signal information at different scales.

3. The method of claim 1, wherein identifying the activity comprises:

accessing classifier data arrangements; and

determining the subset of the extracted features identify portions of motion based on the data in the classifier data arrangements.

4. The method of claim 3, further comprising:

aggregating the portions of motion;

determining a sequence in which the portions of motion are detected; and

identifying the activity based on the sequence.

5. The method of claim 1, wherein extracting the features of the decomposed signal comprises:

forming one or more subsets of the extracted features including signal information at different scales.

6. The method of claim 1, wherein extracting the features of the decomposed signal comprises:

performing wavelet transformations on the one or more decomposed signals; and

forming subsets of the extracted features based on the wavelet transformations.

7. The method of claim 6, wherein performing the wavelet transformations comprises:

performing a discrete wavelet transformation (“

DWT”

) operation.

8. The method of claim 1, further comprising:

forming signals representing one or more of a stillness signal representing a still state, a signal representing an applied force, and a signal representing curvature; and

applying one or more wavelet transformations to form the extracted features from the signals representing one or more of the stillness signal, the signal representing the applied force, and the signal representing curvature.

9. The method of claim 8, further comprising:

forming signals representing one or more of a signal representing a continuity estimator, a signal representing a vertical acceleration, a signal representing an amount of energy, a signal representing a velocity, and one or more signals representing a degree of correlation between at least two signals; and

applying one or more wavelet transformations to form the extracted features from the signals representing the continuity estimator, the vertical acceleration, the amount of energy, the velocity, and the one or more signals representing the degree of correlation.

10. The method of claim 1, wherein generating the one or more decomposed signals comprises:

using one or more estimators.

11. The method of claim 1, further comprising:

identifying characteristics of motion based on the one or more decomposed signals to form motion characteristics data

12. The method of claim 11, wherein identifying the characteristics of motion comprises:

forming signals representing one or more of an orientation, an applied acceleration, and a centripetal acceleration based on the one or more decomposed signals; and

applying one or more wavelet transformations to form the extracted features from the signals representing one or more of the orientation, the applied acceleration, and the centripetal acceleration.

13. The method of claim 1, wherein receiving the data representing the motion sensor signal from the motion sensor further comprises:

receiving accelerometer data representing an acceleration signal from an accelerometer.

14. The method of claim 1, further comprising:

receiving selector control data signals;

determining a degree of accuracy in determining the activity; and

selecting a subset of decomposed signals.

15. The method of claim 14, further comprising:

determining a priority of the decomposed signals; and

forming the subset of decomposed signals based on the priority.

16. An apparatus comprising:

a wearable housing;

a motion sensor configured to sense motion associated with the wearable housing and to generate a motion sensor signal;

a motion processor configured to generate one or more decomposed signals from the motion sensor signal, and further configured to extract a decomposed signal to form extracted features; and

an activity processor configured to identify an activity based on the extracted features.

17. The apparatus of claim 16, wherein the motion processor includes:

a motion characteristic identifier configured to perform wavelet transforms to form the extract features.

18. The apparatus of claim 17, wherein the activity processor comprises:

an activity classifier configured to identify patterns in a database based on a range of probabilities specifying that the feature indicates a presence of a portion of motion.

19. The apparatus of claim 16, wherein the motion processor includes:

a motion characteristic identifier configured to perform wavelet transforms.

20. The apparatus of claim 16, wherein the motion processor includes a digital signal processing core and the activity processor includes a microcontroller core.