Application of electrophysiology-based machine learning in identifying driving fatigue
- VernacularTitle:基于电生理的机器学习在驾驶疲劳识别中的应用
- Author:
Hongyi XIANG
1
;
Xiyan ZHU
1
;
Zhikang LIAO
1
;
Hui ZHAO
1
Author Information
- Publication Type:Review
- Keywords: traffic safety; driving fatigue; machine learning; physiological mechanism; electrophysiological signal
- From: Journal of Environmental and Occupational Medicine 2022;39(4):459-464
- CountryChina
- Language:Chinese
- Abstract: Road traffic accidents (RTA) can cause a large number of casualties and property losses. Driving fatigue is one of the important factors leading to RTA. Electrophysiological signals, as a kind of information feedback for the nervous system to regulate body functions, can reflect drivers’ fatigue state. However, there is a lack of systematic reviews on the current research on electrophysiological signals as information input of machine learning methods for driving fatigue recognition. By investigating fatigue-related literature, the current paper summarized the neural regulation mechanism of fatigue, clarified that driving fatigue is caused by both psychological and physiological loads, recognized inducing factors related to driving fatigue, and summed up electrophysiological signals now in use of driving fatigue recognition, as well as their physiological mechanisms and related indicators. Machine learning algorithms are widely used in identifying driving fatigue. Based on existing studies that used electrophysiological signals as information input source and applied various machine learning algorithms to build driving fatigue identification models, this paper compared the effectiveness of various machine learning algorithms, and described the advantages and disadvantages of supervised machine learning. It is pointed out that suitable classification algorithms should be selected according to sample conditions and model eigenvalues when applied to driving fatigue recognition. In addition, a variety of electrophysiological signals as information sources can help improve the accuracy of a fatigue recognition model, but the increase of model input eigenvalues cannot. Finally, the research progress of identification methods based on electrophysiological signals provided new opportunities for identifying driving fatigue.
