A Residual Neural Network Muscle Fatigue Prediction Model for Overhead Tasks
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NETL
NSTL
万方数据
目的 探讨手过头任务中上肢关节角度与肌肉疲劳之间的关系,并构建了一种基于残差神经网络(residual neural networks,ResNet)的肌肉疲劳预测模型.方法 通过模拟不同作业姿势和不同操作面下的钻孔试验,测量了肌肉最大自主收缩力、最大耐受时间、最大剩余肌力和主观疲劳评分.将测量后数据进行数据处理作为ResNet预测模型的输入,构建残差神经网络模型,以预测肌肉疲劳水平.结果 ResNet模型具有出色的预测精度,均方根误差(root mean square error,RMSE)为0.028,相较于传统的BP神经网络(RMSE=0.053)和MLP多层感知器神经网络(RMSE=0.059),其误差更小,拟合更好.结论 提出的残差神经网络肌肉疲劳预测模型能够有效准确地预测肌肉疲劳,为提高工作效率、减少工作相关肌肉骨骼疾病风险提供了有力支持.
Objective To investigate the relationship between upper limb joint angles and muscle fatigue in overhead tasks and develop a muscle fatigue prediction model based on residual neural networks(ResNet).Methods Through the simulation of drilling experiments performed with different working postures and on different operating surfaces,the maximum voluntary contraction,maximum endurance time,maximum residual muscle force,and subjective fatigue ratings were measured.The collected data were processed and used as input for the ResNet prediction model,which was constructed to predict muscle fatigue levels.Results The ResNet model exhibited outstanding predictive accuracy,with a root mean square error(RMSE)of 0.028.Compared with traditional backpropagation neural networks(RMSE=0.053)and multilayer perceptron neural networks(RMSE=0.059),they displayed smaller errors and better fitting.Conclusions The proposed residual neural network muscle fatigue prediction model can effectively and accurately predict muscle fatigue,providing strong support for improving work efficiency and reducing the risk of work-related musculoskeletal disorders.
NETL
NSTL
万方数据
