Bioinspired Head-To-Shoulder Reference Frame Transformation for Movement-Based Arm Prosthesis Control

Abstract

Movement-based strategies are being explored as alternatives to unsatisfactory myoelectric controls for transhumeral prostheses. We recently showed that adding movement goals to shoulder information enabled Artificial Neural Networks (ANNs), trained on natural arm movements, to predict distal joints so well that transhumeral amputees could reach as with their valid arm in Virtual Reality (VR). This control relies on the object's pose in a shoulder-centered reference frame, whereas it might only be available in a head-centered reference frame through gaze-guided computer vision. Here, we designed two methods to perform the required head-To-shoulder transformation from orientation-only data, possibly available in real-life settings. The first involved an ANN trained offline to do this transformation, while the second was based on a bioinspired space map with online adaptation. Experimental results on twelve participants controlling a prosthesis avatar in VR demonstrated persistent errors with the first method, while the second method effectively encoded the transition between the two frames. The effectiveness of this second method was also tested on six transhumeral amputees in VR, and a physical proof of concept was implemented on a teleoperated robotic platform with computer vision. Those advances represent necessary steps toward the deployment of movement-based control in real-life scenarios.