An Application of Particle Swarm Algorithms to optimize Hidden Markov Models for Driver Fatigue Identification

A hidden Markov model (HMM) has been applied to describe the dynamic process of driver fatigue over time. However, during the HMM training, the initial value selection of the confusion matrix has a great influence on the accuracy ofthe HHM, which brings a lot of inconveniences to the practical application. Therefore, this study applied a particle swarm optimization (PSO) algorithm to simplify the training process without affecting the accuracy of the HMM. In the beginning, an improved HMM was built based on the PSO algorithm, and then was trained by the adoption of the collected data of percentage of eye closing time over a certain period (PERCLOS), which was measured from twenty participants in a driving simulator experiment. Finally, the improved model was compared with the original model, and it didn’t require the initial value selection process based on the prior condition to achieve the global optimum and improve accuracy. It indicates that the proposed method can provide an effective way for driver fatigue identification.