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 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 θformed between the signal vector {right arrow over (s)} and respective ones of the gesture template vectors {right arrow over (g)}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.