EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable 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 / Moin, Ali*; Zhou, Andy; Rahimi, Abbas; Benatti, Simone; Menon, Alisha; Tamakloe, Senam; Ting, Jonathan; Yamamoto, Natasha; Khan, Yasser; Burghardt, Fred; Benini, Luca; Arias, Ana C.; Rabaey, Jan M.. - ELETTRONICO. - 2018-:(2018), pp. 8351613.1-8351613.5. (Intervento presentato al convegno 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 tenutosi a ita nel 2018) [10.1109/ISCAS.2018.8351613].
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
Benatti, Simone;Benini, Luca;
2018
Abstract
EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.