There are some problems in driver fatigue detection research based on multi-source physiological signals,such as the redundancy of characteristic information and the influence of wearing multiple sensors on driver operation.Therefore,a feature selection algorithm combining Particle Swarm Optimization(PSO) algorithm and Sequential Backward Selection(SBS) is proposed.The penalty term of signal source number is added to the fitness function to reduce the number of sensors while reducing the feature dimension.According to the characteristics of the classifier used,the fitness function is simplified and the efficiency of the feature selection algorithm is improved.The signal selection bit is added to the definition of particle to improve the signal screening.Experimental results show that this algorithm uses an average of 2 signals and 16.1 features,and can achieve an accuracy of 95.3% in fatigue driving detection.
multi-source physiological signals,
hybrid feature selection,
Particle Swarm Optimization(PSO),
Sequential Backward Selection(SBS)