基于改进小波去噪和长短时记忆网络的肌肉疲劳识别模型研究

Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory

肌肉疲劳状态自动识别技术在运动学和康复医学领域具有广泛的应用。本文针对采集的表面肌电(sEMG)信号噪声干扰多、现有肌肉疲劳识别模型准确度不高等问题,基于sEMG信号开展循环抗阻训练过程中的下肢肌肉疲劳识别研究。首先,提出一种改进型小波阈值函数去噪算法对采集的sEMG信号进行处理;然后,基于长短时记忆神经网络(LSTM)构建肌肉疲劳状态识别模型,利用Holdout方法评估疲劳识别模型的性能;最后,将本研究提出的改进型小波阈值函数去噪方法的去噪效果与传统小波阈值去噪方法对比,将本文提出的肌肉疲劳识别模型的性能与粒子群优化支持向量机(PSO-SVM)和卷积神经网络(CNN)算法的识别性能进行对比。结果表明:新型小波阈值函数相比于硬、软阈值函数具有更好的去噪效果;在识别肌肉疲劳状态准确度方面LSTM网络模型分别比PSO-SVM和CNN识别分类算法高4.89%和2.47%。本文提出的sEMG信号去噪方法和肌肉疲劳识别模型对于康复训练和运动过程中的肌肉疲劳监测具有重要意义。

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography(sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal.Then, we build a muscle fatigue state recognition model based on long short-term memory(LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine(PSO-SVM) and convolutional neural network(CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.