A novel motion estimate method of human joint with EMG-driven model

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state-space model is developed to estimate joint angular velocities and angles throughout elbow flexion/extension. The state equation of the model combines the Hill-based muscle model with the forward dynamics of joint movement, and expresses the kinematic variables as a function of neural activation levels. Then, EMG features including integral of absolute value and waveform length are extracted, and two quadratic equations which associate the kinematic variables with EMG features are fitted to represent the measurement equation. Based on the proposed model, the joint angular velocities and angles are estimated just using the EMG signals with the Extended Kalman Filter (EKF), and the estimation results are used to control a manipulator. The experimental results demonstrate the efficiency of EMG-based motion control with the proposed model