Performance of lower-extremity exercise-based muscle fatigue and recovery model

Abstract

Repeated or prolonged muscle force exertion leads to the decrease of maximum muscle voluntary contraction force, denoted as muscle fatigue. It will increase the risk of injury if the accumulation of muscle fatigue is not resolved. The detection and assessment of muscle fatigue are important for preventing injuries. However, the complexity of neurology and physiology factors of the human body make it challenging to investigate into musculoskeletal system and model muscle fatigue analytically. Several numerical muscle fatigue models have been developed for the evaluation and prediction of muscle fatigue. However, some of them are developed in static contraction conditions, which is not validated in dynamic conditions, and some others are limited to some simple reciprocal single limb movements. A more generalized muscle fatigue and recovery model need to be developed, which can be applied to both static and dynamic contractions. In reality, human motion is more likely to be dynamic motions. Also, there was a lack of models which considered multiple physiological phenomena change during muscle fatigue and recovery. Thus, a generalized muscle fatigue model that is computationally friendly and reflects multiple physiological changes needs to be developed. The objective of this thesis was to develop a muscle fatigue and recovery model on the basis of the existing muscle model considering the physiological change. The model is proposed by introducing some muscle parameters which can represent the physiological phenomena, and experiments were conducted on 30 subjects for one-legged leg extension exercise to determine the muscle parameters. Surface electromyography was recorded to analyze the change in muscle activity during maximum voluntary isometric contraction, submaximal concentric contraction, and eccentric contraction. The change of maximum voluntary contraction force of the knee extensor at a knee angle of 90 degrees before and after the fatiguing protocol were recorded, as well as the number of leg extension done. The muscle parameters were determined by calculating the rate of fatigue and recovery by fitting the simulation data obtained in the musculoskeletal simulation environment to the measured data, and the muscle fatigue model with and without multiple parameters that represents the physiological phenomena was compared.