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.
At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were used as inputs to the fuzzy inference system (FIS) to construct the fuzzy decision tree (FDT) output active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper’s method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.