Study On Driving Fatigue Recognition Method Based On Smartwatch And Smartphone Sensors

Driving fatigue recognition is one of the important ways to improve road traffic safety.In the existing methods,the driver external characteristics-based method is easily affected by environmental factors such as illumination,and the physiological characteristics-based method is mainly applied in medical field because of its high equipment cost and intrusiveness to drivers.In recent years,the driver operation and vehicle state-based method has been paid more attention by many researchers,which can effectively avoid these shortcomings of the former two.However,this method needs to install a variety of sensors on the vehicle.Some of the fatigue features are not easy to obtain in practice,and most of researches are still in the stage of simulation research.Therefore,it is of great significance to study a driving fatigue recognition method for realizing fatigue driving monitoring and control that does not rely on on-board sensors.Considering that smartwatch and smartphone are now widespread and can effectively monitor the movement of objects,based on the driver operation and vehicle state-based method,this paper uses smartwatch and smartphone to collect drivers' hand behavior and vehicle state data respectively.A more convenient and practical method of driving fatigue recognition is established by analyzing data feature of two devices under different driver states,extracting effective fatigue features representing different driver states,selecting the optimal combination of fatigue features,establishing and improving the fatigue recognition model.The main research work of this paper includes the following three aspects:Firstly,considering that different data information has different fatigue features,after analyzing the fluctuation difference of smartwatch and smartphone sensors data,this paper extracts a variety of feature indexes from these sensors data by three ways: time domain feature method,frequency domain feature method and sample entropy feature method,and uses one-way ANOVA to test the significance of these indexes.Several effective fatigue feature indexes are obtained after the validity of the feature index is quantified.Secondly,aiming at the influence of vehicle driving state information on drivers' steering operation information collected by smartwatch in real-vehicle,based on threedimensional coordinate system rotation method,this paper designs a method of separating this part of vehicle driving state information from driver steering operation information in real-vehicle by using the vehicle driving state information collected by smartphone,which lays a foundation for extracting effective fatigue features in real-vehicle condition.Thirdly,in order to establish a fatigue recognition model with high accuracy,all of the fatigue features extracted from smartwatch and smartphone sensors data are used to optimally select by sequential forward floating selection algorithm,and the evaluation criterion function of feature optimization is established by the classification accuracy of support vector machine,RBF neural network and K-nearest neighbor models respectively.The fatigue recognition model is established based on the classification model and optimal features with the highest accuracy.On this basis,the influence of driver differences on the model is further analyzed,and the model is improved.