Practical Multiple Model Switching sEMG Decoders for Motion Intention Description in Rehabilitation Robot Control

Abstract

Accurate decoding of human motion intention from surface electromyography (sEMG) is crucial for smooth and responsive rehabilitation robot control. This study develops a multiple-model sEMG decoder incorporating a practical bumpless switching strategy that enables seamless transitions among submodels for complex multi-joint movements. The method identifies a minimal common state representation across submodels with different dimensions, ensuring stable and continuous output without abrupt changes during task transitions. Several state-resetting schemes are proposed and experimentally compared in both numerical simulations and real-time human–robot cooperative arm motion tasks. Results demonstrate that the proposed decoder maintains smooth force estimation and robust performance across diverse movement conditions, supporting its applicability to real-time rehabilitation control. Due to safety constraints regarding potential abrupt switching in human-in-the-loop trials, a limited cohort was used; consequently, these findings should be interpreted as exploratory.