Abstract
The purpose of this study is to assess drivers’ upcoming decisions to collision warnings by analyzing their pupil and electromyography (EMG) responses in a real driving environment. Twenty male college students participated in this study. Tobii Glasses2 and electromyography MYO armbands were used to collect the physiological data. Forward collision warning (FCW) and lane departure warning (LDW) were generated from aftermarket CAT devices. According to the results, we found that different fluctuating patterns of pupil and electromyography responses exist when drivers responded to a collision warning. The potential causality between pupil diameter changes and normalized EMG could be applied as a valid indicator of drivers’ different cognitive status to the responded warning and ignored warning, which contains valuable or useless information. Findings from this study will contribute to future algorithm development in a next-generation smart vehicle that can not only identify and predict drivers’ upcoming responses but also customize warning functions based on drivers’ status.
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Yang, X., Kim, J.H. (2022). Pupil and Electromyography (EMG) Responses to Collision Warning in a Real Driving Environment. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_32
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DOI: https://doi.org/10.1007/978-3-031-05409-9_32
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