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KMID : 1137820220430010027
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의공학회지
2022 Volume.43 No. 1 p.27 ~ p.34
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Prediction of Alcohol Consumption Based on Biosignals and Assessment of Driving Ability According to Alcohol Consumption
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Park Seung-Won
Choi Jun-Won
Kim Tae-Hyun
Seo Jeong-Hun
Jeong Myeon-Gyu
Lee Kang-In
Kim Han-Sung
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Abstract
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Drunk driving defines a driver as unable to drive a vehicle safely due to drinking. To crack down on drunk driving, alcohol concentration evaluates through breathing and crack down on drinking using S-shaped courses. A method for assessing drunk driving without using BAC or BrAC is measurement via biosignal. Depending on the individual specificity of drinking, alcohol evaluation studies through various biosignals need to be conducted. In this study, we measure biosignals that are related to alcohol concentration, predict BrAC through SVM, and verify the effectiveness of the S-shaped course. Participants were 8 men who have a driving license. Subjects conducted a d2 test and a scenario evaluation of driving an S-shaped course when they attained BrAC’s certain criteria. We utilized SVR to predict BrAC via biosignals. Statistical analysis used a one-way Anova test. Depending on the amount of drinking, there was a tendency to increase pupil size, HR, normLF, skin conductivity, body temperature, SE, and speed, while normHF tended to decrease. There was no apparent change in the respiratory rate and TN-E. The result of the D2 test tended to increase from 0.03% and decrease from 0.08%. Measured biosignals have enabled BrAC predictions using SVR models to obtain high Figs in primary and secondary cross-validations. In this study, we were able to predict BrAC through changes in biosignals and SVMs depending on alcohol concentration and verified the effectiveness of the S-shaped course drinking control method.
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KEYWORD
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Breath alcohol concentration, Driving simulator, Biosignals, Autonomic nervous system, Support vector machine
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