One shot learning based drivers head movement identification using a
  millimetre wave radar sensor
Hong Nhung Nguyen Seongwook Lee Tien Tung Nguyen Yong Hwa Kim
Concentration of drivers on traffic is a vital safety issue; thus, monitoring
a driver being on road becomes an essential requirement. The key purpose of
supervision is to detect abnormal behaviours of the driver and promptly send
warnings to him her for avoiding incidents related to traffic accidents. In
this paper, to meet the requirement, based on radar sensors applications, the
authors first use a small sized millimetre wave radar installed at the steering
wheel of the vehicle to collect signals from different head movements of the
driver. The received signals consist of the reflection patterns that change in
response to the head movements of the driver. Then, in order to distinguish
these different movements, a classifier based on the measured signal of the
radar 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 of
driver's head movements. The experimental results indicate that the proposed
method can classify the four types of cases according to the various head
movements of the driver with a high accuracy reaching up to 100. In addition,
the classification performance of the proposed method is significantly better
than that of the convolutional neural network model.