At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.
Sit-stand movement is one of the most common movement behaviors of the human body. The knee joint is the main bearing joint of this movement. Thus, the dynamic analysis of knee joint during this movement has deeply positive influences. According to the principle of moment balance, the dynamics of the knee joint during the movement were analyzed. Furthermore, combined with the data obtained from optical motion capture and six-dimensional ground reaction force test, the curve of knee joint torque was calculated. To verify the accuracy of the analysis of dynamic, the human body model was established, the polynomial equations of angle and angular velocity were fitted according to the experimental data, and the knee joint simulation of the movement was carried out. The result revealed that in terms of range and trend, the theoretical data and simulation data were consistent. The relationship between knee joint torque and ground reaction force was revealed based on the variation law of knee joint torque. During the sit-stand movement, the knee joint torque and the ground reaction force were directly proportional to each other, and the ratio was 5 to 6. In the standing process, the acceleration first increased and then decreased and finally increased in reverse, and the maximum knee torque occurred at an angle of about 140°. In the sitting process, the torque was maximized in the initial stage. The results of the dynamics analysis of knee joint during sit-stand movement are beneficial to the optimal design and force feedback control of seated rehabilitation aids, and can provide theoretical guidance for knee rehabilitation training.