Estimation of Phantom Limb Musculoskeletal Mechanics After Targeted Muscle Reinnervation: Towards Online Model-Based Control of Myoelectric Bionic Limbs* Resrach supported by ERC Advanced Grant DEMOVE (267888).

Abstract

Upper limb loss substantially impacts on the quality of life of thousands of individuals worldwide. Current advanced treatments rely on myoelectric prostheses controlled by electromyograms (EMG). Despite advances in surgical procedures (i.e. targeted muscle reinnervation) as well as in electrode design and bio-electric signal sampling, current myocontrol schemes provide limited re-gain of functionality and lack of bio-mimesis. Current solutions create mappings between EMG and prosthesis joint angles, disregarding the underlying neuromusculoskeletal processes. The poor performance of these approaches determines high rejection rates (40-50%) of myoelectric bionic limbs. This paper presents a biomimetic paradigm for active prosthesis control. It encompasses a modelling formulation that simulates the amputee's phantom limb musculoskeletal dynamics as controlled by high-density EMG-extracted neural activations to muscles. We demonstrate how this technique can be applied to a transhumeral amputee offline to decode musculoskeletal function in the phantom elbow and wrist offline. Moreover, we provide preliminary data showing how this technique can be operated online on intact-limbed individuals. The proposed paradigm represents an important step towards next-generation bionic limbs that can mimic human biological limb functionality and robustness.