A State-Space EMG Model for the Estimation of Continuous Joint Movements

Abstract

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper. While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly. EMG features are developed to construct measurement equations for the extended HMM to form a state-space model. With the state-space HMM, a normal closed-loop prediction–correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances. Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads. Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements.