Systems, articles, and methods for gesture identification in wearable electromyography devices

Patent metadata

Field	Value
Publication number	US-9367139-B2
Application number	US-201414567826-A
Country	US
Kind code	B2
Filing date	Dec 11, 2014
Priority date	Dec 12, 2013
Publication date	Jun 14, 2016
Grant date	Jun 14, 2016

Abstract

Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory storing data and/or instructions for performing gesture identification. The wearable EMG device detects signals when a user performs a physical gesture and characterizes a signal vector {right arrow over (s)} based on features of the detected signals. A library of gesture template vectors G is stored in the memory of the wearable EMG device and a respective property of each respective angle θ i formed between the signal vector {right arrow over (s)} and respective ones of the gesture template vectors {right arrow over (g)} i is analyzed to match the direction of the signal vector {right arrow over (s)} to the direction of a particular gesture template vector {right arrow over (g)}*. The accuracy of the gesture identification may be enhanced by performing multiple iterations across multiple time-synchronized portions of the EMG signal data.

First claim

The invention claimed is:

1. A method of operating a wearable electromyography (“EMG”) device, wherein the wearable EMG device includes a set of EMG sensors and a processor communicatively coupled to the set of EMG sensors, the method comprising:
detecting muscle activity of a user of the wearable EMG device by the set of EMG sensors, wherein the muscle activity corresponds to a user-performed gesture;
in response to detecting muscle activity of the user by the set of EMG sensors, providing a set of signals from the set of EMG sensors to the processor;
characterizing the set of signals as a first signal vector {right arrow over (s)} 1 by the processor;
determining a property of a first angle θ 1 formed between the first signal vector {right arrow over (s)} 1 and a first gesture template vector {right arrow over (g)} 1 by the processor, wherein the first gesture template vector {right arrow over (g)} 1 represents a first gesture; and
identifying the user-performed gesture by the processor, wherein identifying the user-performed gesture by the processor includes identifying the first gesture as the user-performed gesture by the processor if, at least, the property of the first angle θ 1 satisfies a criterion.

2. The method of claim 1 wherein characterizing the set of signals as a first signal vector {right arrow over (s)} 1 by the processor includes determining at least one feature of each signal in the set of signals by the processor, each at least one feature selected from the group consisting of: an average value of the signal, a mean value of the signal, a median value of the signal, a mode value of the signal, a maximum value of the signal, a minimum value of the signal, a standard deviation of the signal, a mean power frequency of the signal, and a root mean squared (“RMS”) value of the signal.

3. The method of claim 1 wherein determining a property of a first angle θ 1 formed between the first signal vector {right arrow over (s)} 1 and a first gesture template vector {right arrow over (g)} 1 by the processor includes determining, by the processor, at least one property selected from the group consisting of: a magnitude of the first angle θ 1 , a cosine of the first angle θ 1 , and a sine of the first angle θ 1 .

4. The method of claim 1 wherein the property of the first angle θ 1 does not satisfy the criterion, and wherein the method further comprises:
determining a property of a second angle θ 2 formed between the first signal vector {right arrow over (s)} 1 and a second gesture template vector {right arrow over (g)} 2 by the processor, wherein the second gesture template vector {right arrow over (g)} 2 represents a second gesture, and wherein identifying the user-performed gesture by the processor includes:
identifying the second gesture as the user-performed gesture by the processor if, at least, the property of the second angle θ 2 satisfies the criterion.

5. The method of claim 4 wherein the property of the second angle θ 2 does not satisfy the criterion, and wherein the method further comprises:
until an angle θ* having a property that satisfies the criterion is identified, iteratively:
determining a property of an angle θ i formed between the first signal vector {right arrow over (s)} 1 and an i th gesture template vector {right arrow over (g)} i by the processor, wherein i>2 and the i th gesture template vector {right arrow over (g)} i represents an i th gesture;

and wherein, in response to identifying an angle θ* having a property that satisfies the criterion, identifying the user-performed gesture by the processor includes:
stopping the iteration; and
identifying, by the processor, the gesture that is represented by a gesture template vector {right arrow over (g)}* corresponding to the angle θ* that satisfies the criterion as the user-performed gesture.

6. The method of claim 1 , further comprising:
for each gesture template vector {right arrow over (g)} i in a library of gesture template vectors G, each gesture template vector {right arrow over (g)} i representing a respective gesture, determining a property of an angle θ i formed between the first signal vector {right arrow over (s)} 1 and the gesture template vector {right arrow over (g i )} by the processor, wherein determining a property of an angle θ i formed between the first signal vector {right arrow over (s)} 1 and the gesture template vector {right arrow over (g)} i by the processor for each gesture template vector {right arrow over (g)} i in the library of gesture template vectors G includes determining a property of a first angle θ 1 formed between the first signal vector {right arrow over (s)} 1 and a first gesture template vector {right arrow over (g)} 1 by the processor; and wherein:
identifying the user-performed gesture by the processor includes identifying, by the processor, a gesture represented by a gesture template vector {right arrow over (g)} i from the library of gesture template vectors G for which the property of the angle θ i satisfies a criterion, wherein identifying, by the processor, a gesture represented by a gesture template vector {right arrow over (g)} i from the library of gesture template vectors G for which the property of the angle θ i satisfies a criterion includes identifying the first gesture as the user-performed gesture by the processor if, at least: i) the property of the first angle θ 1 satisfies the criterion and ii) the property of the first angle θ 1 better satisfies the criterion than the property of any other angle θ i .

7. The method of claim 1 wherein the wearable EMG device further includes at least one inertial sensor, and wherein the method further comprises:
detecting motion of the wearable EMG device by the at least one inertial sensor, wherein the motion corresponds to the user-performed gesture;
in response to detecting motion of the wearable EMG device by the at least one inertial sensor, providing at least one signal from the at least one inertial sensor to the processor; and
processing the at least one signal from the at least one inertial sensor by the processor, and wherein identifying the user-performed gesture by the processor includes identifying the user-performed gesture by the processor based at least in part on an outcome of the processing the at least one signal from the at least one inertial sensor by the processor.

8. The method of claim 1 , further comprising:
capturing a respective time-synchronized first portion of each signal in the set of signals by the processor, wherein characterizing the set of signals as a first signal vector {right arrow over (s)} 1 by the processor includes characterizing the time-synchronized first portions of the signals in the set of signals as the first signal vector {right arrow over (s)} 1 by the processor;
capturing a respective time-synchronized second portion of each signal in the set of signals by the processor;
characterizing the time-synchronized second portions of the signals in the set of signals as a second signal vector {right arrow over (s)} 2 by the processor;
determining a property of a second angle φ 1 formed between the second signal vector {right arrow over (s)} 2 and the first gesture template vector {right arrow over (g)} 1 by the processor; and wherein:
identifying the user-performed gesture by the processor includes identifying the first gesture as the user-performed gesture by the processor if, at least: i) the property of the first angle θ 1 satisfies the criterion, and ii) the property of the second angle φ 1 satisfies the criterion.

9. The method of claim 1 wherein the wearable EMG device further includes a non-transitory processor-readable storage medium communicativel

Assignees

• Thalmic Labs Inc

Inventors

• Ataee Pedram
• Aleem Idris S
• Bailey Matthew

Classifications

• G06F3/017 Primary

  Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883) · CPC title

• G06F3/0346

  with detection of the device orientation or free movement in a three-dimensional [3D] space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors · CPC title

• G06F3/014

  Hand-worn input/output arrangements, e.g. data gloves · CPC title

• G06F3/015

  Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection · CPC title

• G06F3/016

  Input arrangements with force or tactile feedback as computer generated output to the user · CPC title