The physical load of the Human body during Motion with BP Neural Network
Authors: Biyun Zhou, Xue Lihao, Xiaopeng Liu, Qing Yang, Liangsheng Ma, Li Ding
Abstract: Background: Unreasonable tasks will increase the person’s physical load, leading to safety accidents and occupational diseases. To ensure a reasonable physical load and improve the operational efficiency of the person as far as possible, it is necessary to predict and evaluate the physical load of workers in real-time.Objective: A prediction model of the physical load intensity of the human body based on a neural network was established, and its effectiveness was verified.Methods: Twelve volunteers completed four movements walking, jogging, climbing, and jumping. The surface electromyography (sEMG) on the left and right sides of the rectus femoris and biceps femoris was measured, and the motor posture of volunteers was obtained by Vicon, the joint torque, maximum muscle activity, and muscular force parameters were calculated based on the reverse dynamic model of human motion. The sEMG eigenvalue and mechanical load parameters in different postures were considered input and output, respectively, and 80% of all data were used as the training set and the rest as the validation set.Results: In this study, we found that the hip joint, knee joint, and ankle joint have a sizeable joint torque during movement, in which the joint torque of the ankle joint is the largest and twice human body weight at its peak. Besides, a larger muscle load occurs at the beginning and end of contact between the human foot and the ground, and the muscle strength of the rectus femoris was significantly higher than that of the biceps femoris (p<0.05). The number of neurons in the input layer, an output layer, and a hidden layer of the model is 32, 13, and 12, respectively. This study found that the prediction error of maximum muscle activity was 6.4%. The average prediction error of joint torque was 8.7%, and the prediction error of the muscular force of the rectus femoris muscle was no more than 9.5%. This model can reasonably predict the physical load of the human body.Conclusions: A workload evaluation model based on the BP neural network was established in this research, which can analyze the biomechanics of the human body in motion and judge the human body’s physical load effectively according to the EMG signal.Application: This model can measure the body load of soldiers and firefighters in real-time during task training and provide a reference for task design.
Keywords: BP neural network, sEMG, biomechanics, muscular load
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