[논문]생체 신호 기반 음주량 예측 및 음주량에 따른 운전 능력 평가

박승원; 최준원; 김태현; 서정훈; 정면규; 이강인; 김한성

doi:10.9718/jber.2022.43.1.27

생체 신호 기반 음주량 예측 및 음주량에 따른 운전 능력 평가
Prediction of Alcohol Consumption Based on Biosignals and Assessment of Driving Ability According to Alcohol Consumption 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.43 no.1, 2022년, pp.27 - 34

박승원 (연세대학교 의공학과) , 최준원 (연세대학교 의공학부) , 김태현 (연세대학교 의공학부) , 서정훈 (연세대학교 의공학부) , 정면규 (현대자동차) , 이강인 (현대자동차) , 김한성 (연세대학교 의공학과)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (11)

그림 그림 1. 실험 과정 Fig. 1. Design of Experiments
그림 그림 2. 부착된 전극 위치 Fig. 2. Attachment position of electrodes
그림 그림 3. 생체 신호 전처리 과정 Fig. 3. The process of preprocessing biosignals
그림 그림 4. 운전 시뮬레이터 주행 코스 Fig. 4. Driving simulator's driving course
그림 그림 5. d2 테스트 예시 Fig. 5. Example of d2 test
그림 그림 6. SVR 모델 개발 과정 Fig. 6. The process of developing the SVR model
그림 그림 7. 생체신호, 동공크기 결과 Fig. 7. Results of biosignals and PS
그림 그림 8. 운전 시뮬레이터, d2 테스트 결과 Fig. 8. Results of d2 test and driving simulator
그림 그림 9. 데이터의 상관계수 Fig. 9. Correlation coefficient of data
그림 그림 10. (a) 1차 교차 검증, (b) 회귀 모델의 오차 평가 Fig. 10. (a) 1st-order cross-validation, (b) Error evaluation for regression model.
그림 그림 11. (a) 2차 교차 검증, (b) 회귀 모델의 오차 평가 Fig. 11. (a) 2nd-order cross-validation, (b) Error evaluation for regression model

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