A Driver State Detection System-Combining a Capacitive Hand Detection Sensor With Physiological Sensors

• Published in: IEEE transactions on instrumentation and measurement Vol. 66; no. 4; pp. 624 - 636
• Main Authors: Muhlbacher-Karrer, Stephan, Mosa, Ahmad Haj, Faller, Lisa-Marie, Ali, Mouhannad, Hamid, Raiyan, Zangl, Hubert, Kyamakya, Kyandoghere
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Artificial neural networks; automotive applications; Biomedical monitoring; capacitive sensors; cellular neural networks (CNNs); Electrodes; ink-jet printing; Monitoring; Vehicles; Wheels; Wireless communication; Wireless sensor networks
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2016.2640458

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 driver'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 "threefold" 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.