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Concentration of drivers on traffic is a vital safety issue; thus, monitoringa driver being on road becomes an essential requirement. The key purpose ofsupervision is to detect abnormal behaviours of the driver and promptly sendwarnings to him her for avoiding incidents related to traffic accidents. Inthis paper, to meet the requirement, based on radar sensors applications, theauthors first use a small sized millimetre wave radar installed at the steeringwheel of the vehicle to collect signals from different head movements of thedriver. The received signals consist of the reflection patterns that change inresponse to the head movements of the driver. Then, in order to distinguishthese different movements, a classifier based on the measured signal of theradar sensor is designed. However, since the collected data set is not large,in this paper, the authors propose One shot learning to classify four cases ofdriver's head movements. The experimental results indicate that the proposedmethod can classify the four types of cases according to the various headmovements of the driver with a high accuracy reaching up to 100. In addition,the classification performance of the proposed method is significantly betterthan that of the convolutional neural network model.

One shot learning based drivers head movement identification using a millimetre wave radar sensor