US 9,389,694 B2 · Systems, articles, and methods for gesture identification in wearable electromyography devices

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 that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects and determines features of signals when a user performs a physical gesture, and processes the features by performing a decision tree analysis. The decision tree analysis invokes a decision tree stored in the memory, where storing and executing the decision tree may be managed by limited computational resources. The outcome of the decision tree analysis is a probability vector that assigns a respective probability score to each gesture in a gesture library. The accuracy of the gesture identification may be enhanced by performing multiple iterations of the decision tree analysis across multiple time windows of the EMG signal data and combining the resulting probability vectors.

Claims27

Dependent claims

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;
determining a set of features of the set of signals by the processor;
performing a series of evaluations of at least some of the features in the set of features by the processor, wherein each evaluation in the series of evaluations includes evaluating a respective feature in the set of features by the processor, and wherein a first evaluation in the series of evaluations includes evaluating a first feature in the set of features by the processor and each subsequent evaluation in the series of evaluations is based at least in part on an outcome of a previous evaluation in the series of evaluations;
determining a respective probability score of each gesture in a gesture library by the processor based at least in part on an outcome of the series of evaluations;
and
identifying the user-performed gesture by the processor based at least in part on the probability score of at least one gesture in the gesture library.

4 more elements

A wearable electromyography (“EMG”) device comprising:

a set of EMG sensors responsive to muscle activity corresponding to a gesture performed by a user of the wearable EMG device, wherein in response to muscle activity corresponding to a gesture performed by the user, the set of EMG sensors provide a set of signals;
a processor communicatively coupled to the set of EMG sensors;
and
a non-transitory processor-readable storage medium communicatively coupled to the processor, wherein the non-transitory processor-readable storage medium stores processor-executable gesture identification instructions that, when executed by the processor, cause the processor to:
determine a set of features of the set of signals;
perform a series of evaluations of at least some of the features in the set of features by the processor, wherein each evaluation in the series of evaluations includes evaluating a respective feature in the set of features, and wherein a first evaluation in the series of evaluations includes evaluating a first feature in the set of features and each subsequent evaluation in the series of evaluations is based at least in part on an outcome of a previous evaluation in the series of evaluations;
determine a respective probability score of each gesture in a gesture library based at least in part on the series of evaluations;
and
identify the gesture performed by the user based at least in part on the probability score of at least one gesture in the gesture library.

6 more elements

A method of identifying which gesture in a gesture library is performed by a user of 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:

in response to the user performing a gesture while wearing the wearable EMG device, detecting muscle activity of the user by the set of EMG sensors;
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, wherein each signal in the set of signals is provided by a respective one of the EMG sensors in the set of EMG sensors;
until a cumulative probability score that exceeds a threshold value is identified, iteratively:
capturing a respective time-synchronized portion of each signal in the set of signals by the processor;
determining a set of features of the time-synchronized portions of the signals in the set of signals by the processor;
performing a decision tree analysis of the set of features by the processor;
determining a respective probability score of each gesture in the gesture library by the processor;
generating a respective cumulative probability score of each gesture in the gesture library by the processor;
and
comparing the cumulative probability scores in the set of cumulative probability scores to the threshold value by the processor;
and
in response to identifying a cumulative probability score that exceeds a threshold value:
stopping the iteration;
and
returning a gesture in the gesture library that corresponds to the cumulative probability score that exceeds the threshold value by the processor.

12 more elements

A wearable electromyography (“EMG”) device comprising:

a set of EMG sensors responsive to muscle activity corresponding to a gesture performed by a user of the wearable EMG device, wherein in response to muscle activity corresponding to a gesture performed by the user, the set of EMG sensors provide a set of signals;
a processor communicatively coupled to the set of EMG sensors;
and
a non-transitory processor-readable storage medium communicatively coupled to the processor, wherein the non-transitory processor-readable storage medium stores processor-executable gesture identification instructions that, when executed by the processor, cause the processor to:
i) until a cumulative probability score that exceeds a threshold value is identified, iteratively:
capture a respective time-synchronized portion of each signal in the set of signals;
determine a set of features of the time-synchronized portions of the signals in the set of signals;
perform a decision tree analysis of the set of features;
determine a respective probability score of each gesture in the gesture library;
generate a respective cumulative probability score of each gesture in the gesture library;
and
compare the cumulative probability scores in the set of cumulative probability scores to the threshold value;
and
ii) in response to identifying a cumulative probability score that exceeds a threshold value:
stop the iteration;
and
return the gesture in the gesture library that corresponds to the cumulative probability score that exceeds the threshold value.

14 more elements

Dependencies are inferred from the claim text.

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

Life of this patent

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

Life of this patent

Abstract

Claims27