计算机科学
模态(人机交互)
脑电图
背景(考古学)
人工智能
无聊
可靠性(半导体)
特征提取
卡帕
警报
神经生理学
模式识别(心理学)
特征(语言学)
作者
Abdulhamit Subasi,Aditya Saikia,Kholoud Bagedo,Amarprit Singh,Anil Hazarika
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:18 (10): 6602-6609
被引量:2
标识
DOI:10.1109/tii.2022.3167470
摘要
Globally, 14-20% of road accidents are mainly due to driver fatigue caused of which are instance sickness, travelling for long distance, boredom as a resistance of driving along the same route consistently and lack of enough sleep. This paper presents a flexible analytic wavelet transform based advanced machine learning method using single modality neurophysiological brain electroencephalogram signals to detect the driver fatigues and to alarm the driver at the earliest for preventing the risks during driving. First signals of undertaking study groups are subjected to the FAWT that separates the signals into low and high pass channels. Subsequently relevant sub-band frequency components with proper setting of tuning parameters are extracted. Then, comprehensive low order features which are statistically significant for p 0.05, are evaluated from the input subband searched space and embedded them to various ensemble methods under multiboost strategy. Results are evaluated in terms of various parameters including accuracy, F-score, AUC and kappa. Results show that the proposed approach is promising in classification and it achieves optimum individual accuracies of 97.1% and 97.9% in categorizing FATIGUE and REST states with F- score of 97.5%, AUC of 0.975% and kappa of 0.950%. Comparison of the proposed method with the prior methods in the context of feature, accuracy, modality profiles undertaken, indicates the effectiveness and reliability of the proposed method for real-world applications
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