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

摘要

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.

主权项

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; 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.