Instinctive real-time sEMG-based control of prosthetic hand with reduced data acquisition and embedded deep learning training

Abstract

Achieving instinctive multi-grasp control of prosthetic hands typically still requires a large number of sensors, such as electromyography (EMG) electrodes mounted on a residual limb, that can be costly and time consuming to position, with their signals difficult to classify. Deep-learning-based EMG classifiers however have shown promising results over traditional methods, yet due to high computational requirements, limited work has been done with in-prosthetic training. By targeting specific muscles non-invasively, separating grasping action into hold and release states, and implementing data augmentation, we show in this paper that accurate results for embedded, instinctive, multi-grasp control can be achieved with only 2 low-cost sensors, a simple neural network, and minimal amount of training data. The presented controller, which is based on only 2 surface EMG (sEMG) channels, is implemented in an enhanced version of the OLYMPIC prosthetic hand. Results demonstrate that the controller is capable of identifying all 7 specified grasps and gestures with 93% accuracy, and is successful in achieving several real-life tasks in a real world setting.