Applying neural network analysis on heart rate variability data to assess driver fatigue

被引：294
作者：
Patel, M. [1 ]
Lal, S. K. L. [1 ]
Kavanagh, D. [1 ]
Rossiter, P. [2 ]
机构：
[1] Univ Technol Sydney, Ultimo 2007, Australia
[2] Forge Grp, Ultimo 2007, Australia
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 06期
基金：
澳大利亚研究理事会;
关键词：
Heart rate variability; Neural network; Driver fatigue; Spectral analysis;
D O I：
10.1016/j.eswa.2010.12.028
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver's alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：7235 / 7242
页数：8
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