Driving Fatigue Monitoring via Kernel Sparse Representation Regression With GMC Penalty

Automatic monitoring technology for fatigued driving can greatly reduce traffic accidents caused by drivers due to decreased attention. The use of monitoring algorithms based on physiological signal sensors is of great significance for objective monitoring. In this paper, a novel algorithm with exce...

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Bibliographic Details
Published in:IEEE sensors journal 2022-08, Vol.22 (16), p.16164-16177
Main Authors: Zhang, Xuan, Wang, Dixin, Wu, Hongtong, Lei, Chang, Zhong, Jitao, Peng, Hong, Hu, Bin
Format: Article
Language:English
Subjects:
Algorithms
Biomedical monitoring
Coefficients
Driver fatigue
Electroencephalography
Fatigue
Fatigue driving monitoring
GMC penalty
Hilbert space
Kernel
Kernels
Minimax technique
Monitoring
Physiology
Regression
regression algorithm
Representations
robust recognition
Sensors
sparse representation (SR)
Traffic accidents
Training
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Summary:Automatic monitoring technology for fatigued driving can greatly reduce traffic accidents caused by drivers due to decreased attention. The use of monitoring algorithms based on physiological signal sensors is of great significance for objective monitoring. In this paper, a novel algorithm with excellent performance and strong generalization ability, kernel sparse representation regression based on generalized minimax-concave (GMC-KSRR), is proposed to identify fatigue physiological signals. Specifically, in the proposed algorithm, the training samples of physiological features are mapped in reproducing kernel Hilbert space (RKHS) for linear separability. Then, in RKHS, the sparse coefficients of the test samples are obtained by generalized minimax-concave (GMC) penalty-based sparse representation (SR). Different from the traditional \ell _{{1}} -norm, the GMC penalty we used does not underestimate the high-amplitude component of sparsity coefficients, resulting in a more accurate regression result. Finally, the regression decision is performed in the label subspace according to the obtained sparse coefficients. Meanwhile, a multimodality algorithm based on GMC-KSRR has also been proposed that weighs each modality according to quality for robust fatigue monitoring. Eventually, competitive experimental results on the well-known SEED-VIG dataset against the state-of-the-art methods demonstrate the efficacy of the proposed algorithms in identifying fatigue physiological signals, indicating its powerful application potential in driving fatigue monitoring.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3177931