Analysis and Research on Neck Fatigue Identification in Long-term Flexed Neck Position Population Based on Multi-domain Characteristic Parameters
Maintaining the flexion position for a long time can easily lead to neck muscle fatigue,resulting a soft tissue injury and degenerative changes of the cervical spine.Therefore,it is significant to discriminate the fatigue state of the neck muscles of the workers who bend and sit at the desk for a long time.The wavelet threshold method,Empirical Mode Decomposition(EMD),the combination of EMD and wavelet threshold are used to de-noise the neck EMG of the people who bend the neck for a long time.By calculating root mean square error(RMSE),signal-to-noise ratio(SNR)and operation speed,the three denoising methods are compared and analyzed.The results show that the denoising method the combination of EMD and wavelet threshold has the best signal-to-noise separation ability.Then the time domain,frequency domain and nonlinear characteristic parameters of the neck EMG signal are extracted,and the effective characteristics are determined by analyzing the variation trend of each characteristic parameter with time.Finally,the effective feature vector is used as the input of the classifier,and the convolutional neural network model is established to recognize the fatigue state of the neck muscle,and the classification accuracy reaches 90.48%.
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