A Driver State Detection Systemâ??Combining a Capacitive Hand Detection Sensor With Physiological Sensors
With respect to automotive safety, the driver plays
a crucial role. Stress level, tiredness, and distraction of the
driver are therefore of high interest. In this paper, a driver
state detection system based on cellular neural networks (CNNs)
to monitor the driverâ??s stress level is presented. We propose to
include a capacitive-based wireless hand detection (position and
touch) sensor for a steering wheel utilizing ink-jet printed sensor
mats as an input sensor in order to improve the performance.
A driving simulator platform providing a realistic virtual traffic
environment is utilized to conduct a study with 22 participants
for the evaluation of the proposed system. Each participant is
driving in two different scenarios, each representing one of the
two no-stress/stress driver states. A â??threefoldâ?? cross validation
is applied to evaluate our concept. The subject dependence is
considered carefully by separating the training and testing data.
Furthermore, the CNN approach is benchmarked against other
state-of-the-art machine learning techniques. The results show a
significant improvement combining sensor inputs from different
driver inherent domains, giving a total related detection accuracy
of 92%. Besides that, this paper shows that in case of including
the capacitive hand detection sensor, the accuracy increases by
10%. These findings indicate that adding a subject-independent
sensor, such as the proposed capacitive hand detection sensor,
can significantly improve the detection performance.
KeyWords
Artificial neural networks, automotive applications,
capacitive sensors, cellular neural networks (CNNs), ink-jet
printing.
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