A Surface Electromyography-Driven State-Space Model for Joint Angle Estimation

Abstract

Joint angle estimation during continuous motion based on surface electromyography (sEMG) plays a crucial role in exoskeleton robot applications. To estimate joint angles using sEMG signals, a Hill-based muscle model (HMM) can be incorporated into forward dynamics algorithms. Nevertheless, obtaining muscle parameters remains challenging, and the strongly nonlinear “open-loop” system can lead to significant cumulative errors. To address these issues, this article applies a novel “closed-loop” sEMG-driven state-space model to estimate the elbow joint angle in the human upper limb during continuous motion. The forward kinematics of the human joints are integrated with the HMM, and a torque-based musculoskeletal parameter estimation (TBMPE) is introduced to derive a more accurate state transition function by utilizing angular velocity as a cost function. The observation function is constructed by utilizing sEMG features to design the “closed-loop” sEMG-driven state-space model. Subsequently, the particle filter (PF) algorithm is introduced to stably estimate the joint angles within the strongly nonlinear model. Experiments were conducted based on the elbow joints of six healthy subjects. The experimental results indicate that the proposed new model has an average root mean square error (RMSE) of 0.09 for cases with and without loads. The estimation results are better than those of similar algorithms, proving the validity of the proposed approach.