Fatigue level detection using multivariate autoregressive exogenous nonlinear modeling based on driver body pressure distribution

Prolonged driving causes symptoms of fatigue in drivers and changes their physical condition during driving. The purpose of this paper is to use a force measurement system located in the driver’s seat by force-sensitive resistance pressure sensors in order to record the received information to predict fatigue by learning regression-based models. This system is designed with 16 FSR (Force Sensing Resistor) sensors mounted on the seat and its backrest that records the driver’s body’s data, based on the force exerted by the driver on the seat in standard mode and during driving at various times. Fatigue level prediction is based on the trained nonlinear autoregressive exogenous model. In this procedure, models based on multivariate regression are first trained, and then correctness is checked. In this paper, the fatigue index is divided into five parts from 0 to 100 included fully conscious, slightly tired, moderately tired, very tired, and extremely tired, so the criterion for diagnosis is crossing the 75% of fatigue index and entering the extremely tired range. The results show that nonlinear models based on exogenous autoregressive have better performance than the linear mode, and even in the nonlinear model of NARX neural network, the fatigue of one step ahead is well predictable. A flopped state will be predictable when the body is immersed in the seat due to fatigue, so is far from the standard sitting position and will be in the extremely tired warning range.