A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator's motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-speci ̄c electromyography (EMG)-driven musculoskeletal model to estimate subject's joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-speci ̄c musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identi ̄ed by o®-line optimization methods in order to minimize the di®erences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model's ability to predict knee joint torque with the coe±cient of determination (R2Þ value of 0:934 0:013 and the normalized root-mean-square error (RMSE) of 11:58% 1:44%.