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Exploring Cognitive Distraction of Galvanic Skin Response while Driving: An Artificial Intelligence Modeling

Chiang-Yu Cheng 1, Wesley Shu 2, and Han-Ping Tsen 3
1. School of Big Data Management, Soochow University, Taiwan
2. International Business School, Xi’an Jiaotong-Liverpool University, Suzhou, China
3. Department of Information and Management, National Central University, Taiwan
Abstract—It is quite often that we hear fatal traffic accidents due to driver’s distraction. Car manufactures and researchers are therefore putting their efforts into car safety protection mechanism. However, the application of car safety protection mechanism is frequently hindered by its limitations, such as drivers’ privacy or the high cost of its deployment and each of which leads to rare applications of car safety protection specifically in the field of non-autonomous cognitive distraction. This research proposal intends to apply the sensor of Galvanic Skin Response (GSR) to measure drivers’ non-autonomous cognitive distraction due to the blood glucose variation of diabetes. SVM-RFE will be adopted as the major algorithm to create an alert mechanism with the artificial intelligence concept of supervised machine learning. The researched human-machine sense interaction mechanism can be able to embed into the car computer so that it can detect drivers’ physiological changes during diabetes outbreak and then raise advisable alert and intervention accordingly.
 
Index Terms—cognitive distraction, galvanic skin response, artificial intelligence, supervised machine learning

Cite: Chiang-Yu Cheng, Wesley Shu, and Han-Ping Tsen, "Exploring Cognitive Distraction of Galvanic Skin Response while Driving: An Artificial Intelligence Modeling," Journal of Advances in Information Technology, Vol. 11, No. 1, pp. 35-39, February 2020. doi: 10.12720/jait.11.1.35-39

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