FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking

• Published in: IEEE/ASME transactions on mechatronics Vol. 16; no. 5; pp. 816 - 826
• Main Authors: Qin Zhang, Hayashibe, M., Fraisse, P., Guiraud, D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Bioengineering; Biomechanics; Computer Science; Control systems; Control theory; Electrodes; Electromyography; Engineering Sciences; Evoked electromyography (eEMG); Fatigue; Fatigue (materials); functional electrical stimulation (FES); Kalman filter with forgetting factor; Kalman filters; Life Sciences; Mathematical models; Mechanics; muscle fatigue tracking; Muscles; Neuromuscular stimulation; Physics; Robotics; Spinal cord injuries; Studies; Torque; Torque measurement; torque prediction; Tracking; Muscle Fatigue Tracking; Kalman Filter with Forgetting Factor; Torque Prediction; Functional Electrical Stimulation (FES); Evoked Electromyography (eEMG)
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2011.2160809

This paper investigates a torque estimation method for muscle fatigue tracking, using stimulus evoked electromyography (eEMG) in the context of a functional electrical stimulation (FES) rehabilitation system. Although FES is able to effectively restore motor function in spinal cord injured (SCI) individuals, its application is inevitably restricted by muscle fatigue. In addition, the sensory feedback indicating fatigue is missing in such patients. Therefore, torque estimation is essential to provide feedback or feedforward signal for adaptive FES control. In this paper, a fatigue-inducing protocol is conducted on five SCI subjects via transcutaneous electrodes under isometric condition, and eEMG signals are collected by surface electrodes. A myoelectrical mechanical muscle model based on the Hammerstein structure with eEMG as model input is employed to capture muscle contraction dynamics. It is demonstrated that the correlation between eEMG and torque is time varying during muscle fatigue. Compared to conventional fixed-parameter models, the adapted-parameter model shows better torque prediction performance in fatiguing muscles. It motivates us to use a Kalman filter with forgetting factor for estimating the time-varying parameters and for tracking muscle fatigue. The assessment with experimental data reveals that the identified eEMG-to-torque model properly predicts fatiguing muscle behavior. Furthermore, the performance of the time-varying parameter estimation is efficient, suggesting that real-time tracking is feasible with a Kalman filter and driven by eEMG sensing in the application of FES.