基于HRV分析的可穿戴心电仪精神疲劳检测

Mental fatigue is a key cause of many chronic diseases such as CVD, diabetes and cancer. It is illusive and hard to measure or detect. This research proposes a feasible accurate and cost saving method to detect fatigue level of mental workers via smart wearable devices. In order to detect mental fatigue level, this paper firstly extracted HRV features, then used Man-Whitney U Test to evaluate the statistical significances of HRV features between Normal and Fatigue state. This paper used Random Forest for feature selection to determine the importance of HRV features. The most important HRV features are NN. mean, PNN50, VLF, LF and TP respectively. This paper then took four machine learning algorithms on selected features to predict the state of mental fatigue. The experiments demonstrated the effectiveness of KNN classifier with 75.5% accuracy rate on cross validation and 0.74 AUC.