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EMG-based gesture recognition shows promise for human-machine interaction.Systems are often afflicted by signal and electrode variability which degradesperformance over time. We present an end-to-end system combating thisvariability using a large-area, high-density sensor array and a robustclassification algorithm. EMG electrodes are fabricated on a flexible substrateand interfaced to a custom wireless device for 64-channel signal acquisitionand streaming. We use brain-inspired high-dimensional (HD) computing forprocessing EMG features in one-shot learning. The HD algorithm is tolerant tonoise and electrode misplacement and can quickly learn from few gestureswithout gradient descent or back-propagation. We achieve an averageclassification accuracy of 96.64% for five gestures, with only 7% degradationwhen training and testing across different days. Our system maintains thisaccuracy when trained with only three trials of gestures; it also demonstratescomparable accuracy with the state-of-the-art when trained with one trial.

An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier