An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 12; pp. 3113 - 3120
• Main Authors: Zhao, Yihui, Zhang, Zhiqiang, Li, Zhenhong, Yang, Zhixin, Dehghani-Sanij, Abbas A., Xie, Shengquan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; continuous wrist joint motion; Deep learning; electromyogram signal; Electromyography; Force; forward dynamics; Genetic algorithms; Hill’s muscle model; Joints (anatomy); Learning algorithms; Machine learning; Motion simulation; Muscle contraction; Muscles; Numerical models; Tendons; Wrist
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.3038051

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.