Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Apr 21;22(9):3199. doi: 10.3390/s22093199.
Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.
准确评估身体疲劳对于预防因过度运动、日常运动和专业运动训练中的过度训练而导致的身体损伤至关重要。然而，由于身体疲劳是个体的主观感受，其他人很难客观地评估。心率变异性（HRV）源自心电图（ECG）并受自主神经系统控制，已被证明是一种很有前途的身体疲劳估计指标。在本文中，我们提出了一种基于 HRV 的自动客观的身体疲劳分类新方法。首先，计算了总共 24 个 HRV 特征。然后，提出了一种特征选择方法，用于去除与身体疲劳相关性低的无用特征和与所选特征相关性高的冗余特征。经过特征选择后，选择了最好的 11 个特征，最终用于身体疲劳分类。使用所选特征训练了四种机器学习算法来进行疲劳分类。实验结果表明，使用所选 11 个特征训练的模型可以高精度地分类身体疲劳。更重要的是，这些所选特征可以提供有关识别身体疲劳的重要信息。
