Muscle force prediction during knee flexion/extension using EMG-driven model

Abstract

Predicting muscle force during joint movement is important to gain a better understanding of musculoskeletal system. In this study, an EMG-driven model for predicting the muscle force in lower limb during knee flexion-extension is presented and the rate-effect on muscle model parameters is investigated. The model was based on Hill-type muscle model to describe the contraction mechanism of muscle. Surface electrodes were attached to the subject’s leg to detect the EMG signals and the knee joint angle was measured by an electrogoniometer. The subjects performed a series of knee flexionextension with various movement frequencies. Muscle fiber length, velocity and activation during the movement were used as inputs in the muscle model to predict the muscle force. To study the rate-effect on muscle model parameters, optimization processes were performed to obtain muscle model parameters at various movement frequencies. The external forces calculated from the predicted muscle forces were compared with measured forces from load cell to validate the accuracy of the model. The results showed that the muscle model parameters changed with respect to the movement frequency. In order to improve the accuracy of Hill-type muscle model, various muscle model parameters which change with movement frequency were suggested. Development in muscle model is very useful in studying the musculoskeletal system leads to improvement in diagnostic tool, planning effective exercise training programs and development of rehabilitation procedure.