Wang Zheng, Guan Xiaorong, Li Dingzhe, Jiang Changlong, Bai Yu, Yang Dongrui, He Long
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Hangzhou Zhiyuan Research Institute Co., Ltd., Hangzhou 310013, China.
Sensors (Basel). 2025 Aug 13;25(16):5023. doi: 10.3390/s25165023.
The manual lifting of heavy loads by personnel is susceptible to the development of muscle fatigue, which, in severe cases, can result in the irreversible impairment of muscle function. This study proposes a novel method of signal fusion to analyse muscle fatigue during manual lifting. Furthermore, this study represents the inaugural application of the back-propagation neural network and bidirectional encoder representation from the transformer (BP + BERT) algorithm to the fusion of two sensor inputs for the analysis of muscle fatigue. Lifting action fatigue tests were carried out on 16 testers in this study, with both surface electromyography (sEMG) and mechanomyography (MMG) signals collected as part of the process. The mean power frequency (MPF) eigenvalues were extracted separately for the two signals, and the results of muscle fatigue labelling according to the trend of the MPF eigenpeak were merged to produce three datasets. Subsequently, the three datasets were employed to categorise muscle fatigue classes using the support vector machine and radial basis function (SVM + RBF), support vector machine and bidirectional encoder representation from transformer (SVM + BERT), back-propagation neural network (BP), and back-propagation neural network and bidirectional encoder representation from transformer (BP + BERT) algorithms, respectively. The results of the muscle fatigue classification model demonstrated that the sEMG and MMG fused dataset, imported into the BP + BERT algorithm, exhibited the highest average accuracy of 98.10% for the muscle fatigue classification model. This study indicates that the fusion of sEMG and MMG signals is an effective approach, and the performance of the BP + BERT muscle fatigue classification model is also enhanced.
人员手动搬运重物容易导致肌肉疲劳，在严重情况下，可能会导致肌肉功能不可逆转的损伤。本研究提出了一种新颖的信号融合方法来分析手动搬运过程中的肌肉疲劳。此外，本研究首次将反向传播神经网络和基于变换器的双向编码器表征（BP + BERT）算法应用于融合两个传感器输入以分析肌肉疲劳。本研究对16名测试者进行了举升动作疲劳测试，在此过程中采集了表面肌电图（sEMG）和机械肌电图（MMG）信号。分别提取了两种信号的平均功率频率（MPF）特征值，并根据MPF特征峰的趋势对肌肉疲劳标记结果进行合并，生成了三个数据集。随后，分别使用支持向量机和径向基函数（SVM + RBF）、支持向量机和基于变换器的双向编码器表征（SVM + BERT）、反向传播神经网络（BP）以及反向传播神经网络和基于变换器的双向编码器表征（BP + BERT）算法，利用这三个数据集对肌肉疲劳类别进行分类。肌肉疲劳分类模型的结果表明，导入BP + BERT算法的sEMG和MMG融合数据集在肌肉疲劳分类模型中表现出最高的平均准确率，为98.10%。本研究表明，sEMG和MMG信号的融合是一种有效的方法，并且BP + BERT肌肉疲劳分类模型的性能也得到了提升。
