A close-loop EMG model for continuous joint movements estimation of a rehabilitation robot

A close-loop algorithm based on electromyography (EMG) state-space model and measurement equation is developed for the estimation of continuous joint movements to achieve active control of a lower limb rehabilitation robot. While the general Hill muscle model estimates only joint torque from EMG signals in an open-loop form, we integrate the forward dynamics of joint movement into the Hill model and established a state-space model. A measurement equation is proposed to get the measured value through the inertial measurement unit (IMU). Nonlinear extended Kalman filter is used to combine these two model, and reduced measurement error and process noise. In addition, a load disturbance compensation model is developed by the parameter identification algorithm to reduce the impact of external load changes on the model. The maximum muscle activation of each cycle is used for load disturbance compensation. Comprehensive experiments are conducted on the human leg joint with open-loop experiment, closed-loop experiment and load disturbance compensation verification experiment.

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