DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills

Abstract

Abstract Objective. An active myoelectric interface responds to the user’s muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills. Approach. We propose DistaNet as a neural network-based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users. Main results. Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills. Significance. We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.