Upper limb muscle strength prediction based on motion capture and sEMG data

Abstract

Dyskinesia of upper extremity caused by brain diseases such as stroke brings a heavy burden to the patient. The active exercise is the best method for upper limb exercise rehabilitation. Upper limb muscle strength is an indicator of upper limb exercise rehabilitation evaluation and recovery. This paper proposes a method for predicting muscle strength by acquiring the surface electromyogram (sEMG) signal of the upper extremity muscle group combined with the HILL model for muscle strength prediction. Using the upper limb kinematics data to drive the upper limb muscle model in the OpenSim to simulate the upper limb muscle strength, compared with the muscle strength calculated by the sEMG, verifying that the method of sEMG predicting muscle strength is feasible. The prediction of upper limb muscle strength can provide an evaluation index for the upper limb rehabilitation process. Through the deep excavation of the upper limb sEMG, it can lay a foundation for the development of the upper limb rehabilitation robot driven by the sEMG signal.