생체 신호 기반 음주량 예측 및 음주량에 따른 운전 능력 평가

• Published in: Journal of biomedical engineering research Vol. 43; no. 1; pp. 27 - 34
• Main Authors: 박승원, 최준원, 김태현, 서정훈, 정면규, 이강인, 김한성, Park, Seung Won, Choi, Jun won, Kim, Tae Hyun, Seo, Jeong Hun, Jeong, Myeon Gyu, Lee, Kang In, Kim, Han Sung
• Format: Journal Article
• Language: Korean
• Published: 대한의용생체공학회 01.02.2022
• Subjects: 의공학; Biosignals; Driving simulator; Support vector machine; Autonomic nervous system; Breath alcohol concentration
• ISSN: 1229-0807; 2288-9396

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.