《计算机应用研究》|Application Research of Computers

基于HRV分析的可穿戴心电仪精神疲劳检测

Detection of mental fatigue with wearable ECG devices based on HRV analysis

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作者 黄诗童,张威强,张朋柱
机构 上海交通大学 安泰经济与管理学院,上海 200030
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文章编号 1001-3695(2019)07-038-2093-05
DOI 10.19734/j.issn.1001-3695.2018.05.0262
摘要 精神疲劳是许多慢性疾病如心血管疾病、糖尿病和癌症的关键原因,然而又难以量化评估及测量,提出了一种通过智能穿戴设备检测脑力劳动者疲劳程度的工程可行性的方案。为了检测脑力疲劳程度,通过Man-Whitney U检验评估了HRV各项指标在判断精神疲劳状态的统计显著性,并使用随机森林进行特征选择以确定HRV各项指标的重要性。研究发现,最重要的HRV指标分别是NN.mean、PNN50、VLF、LF和TP。最后采用SVM、nave Bayes、KNN和逻辑回归四种机器学习算法对疲劳状态进行识别,实验证明了KNN分类器最为有效,其交叉验证准确率为75.5%和AUC为0.74。
关键词 精神疲劳检测; HRV; 曼—惠特尼 U检验; 随机森林; 机器学习
基金项目 国家自然科学基金资助项目(91646205,71421002)
上海交通大学中央高校基本科研业务费资助项目(16JCCS08)
本文URL http://www.arocmag.com/article/01-2019-07-038.html
英文标题 Detection of mental fatigue with wearable ECG devices based on HRV analysis
作者英文名 Huang Shitong, Zhang Weiqiang, Zhang Pengzhu
机构英文名 Antai College of Economics & Management,Shanghai Jiao Tong University,Shanghai 200030,China
英文摘要 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 proposed 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 were 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 demonstrate the effectiveness of KNN classifier with 75.5% accuracy rate on cross validation and 0.74 AUC.
英文关键词 mental fatigue detection; HRV; Man-Whitney U test; random forest; machine learning
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收稿日期 2018/5/8
修回日期 2018/6/22
页码 2093-2097,2103
中图分类号 TP399
文献标志码 A