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

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.