지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
오류제보하기
Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver"s drowsiness and waking status in order to develop the classification model for a driver"s drowsiness and waking status using those features.
Background: Driver"s drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver"s drowsiness.
Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation.
Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%).
Conclusion: This study suggests that the classification of driver"s drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features.
Application: The results of this study can be applied to the development of driver"s drowsiness prevention systems.
Background: Driver"s drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver"s drowsiness.
Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation.
Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%).
Conclusion: This study suggests that the classification of driver"s drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features.
Application: The results of this study can be applied to the development of driver"s drowsiness prevention systems.
목차
1. Introduction
2. Method
3. Results
4. Discussion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
머신 비젼을 이용한 졸음 감지 시스템 개발
제어로봇시스템학회 논문지
2016 .04
-
얼굴 특징점 기반의 졸음운전 감지 알고리즘
멀티미디어학회논문지
2016 .11
-
자동차 안전을 위한 히스토그램 이용 졸음 감지 시스템 개발
제어로봇시스템학회 논문지
2015 .02
-
눈의 히스토그램과 에지를 이용한 졸린 상태 감시 시스템 개발
제어로봇시스템학회 각 지부별 자료집
2016 .01
-
레티넥스 이론과 에지를 이용한 졸음 감지 시스템 개발
제어로봇시스템학회 논문지
2016 .09
-
눈 영상의 히스토그램을 이용한 운전자의 졸음 상태 체크 시스템 개발
제어로봇시스템학회 논문지
2015 .04
-
눈의 히스토그램과 에지를 이용한 졸린 상태 감시 시스템 개발
제어로봇시스템학회 논문지
2016 .05
-
얼굴 특징점 검출을 이용한 임베디드 보드에서의 운전자 졸음 검출 시스템
대한전자공학회 학술대회
2019 .11
-
뇌파 검출을 통한 졸음지표 검출 및 소형 측정 장치 개발
한국재활복지공학회 학술대회 논문집
2015 .11
-
외부조명 변화에 강인한 운전자 졸음 감지 시스템
멀티미디어학회논문지
2016 .06
-
클래스 균형을 통한 운전자 감지 향상 기법 연구
대한전자공학회 학술대회
2024 .11
-
Detecting Early Signs of Drowsy Driving using an Artificial Neural Network on Electrocardiography Signals
대한인간공학회지
2022 .02
-
Classification of Driver States using Artificial Neural Network on ECG Signals
대한인간공학회 학술대회논문집
2019 .10
-
얼굴 및 졸음 인식 모델 기반 음주 및 졸음 감지 모니터링 시스템 구현
제어로봇시스템학회 논문지
2025 .04
-
Drowsiness Preventing System using YOLO
INTERNATIONAL CONFERENCE ON FUTURE INFORMATION & COMMUNICATION ENGINEERING
2019 .06
-
멀티 모달 학습을 사용한 시각적 특징 기반의 운전자 졸음 감지
한국통신학회논문지
2018 .07
-
자동차 안전을 위한 졸음 상태 감지 시스템 개발
제어로봇시스템학회 국내학술대회 논문집
2016 .03
-
PERCLOS 기반 자동차 실내 조명 제어에 따른 운전자 졸음 상태 평가
대한인간공학회 학술대회논문집
2022 .10
-
딥 러닝 기반 졸음 운전 방지 시스템
한국정보과학회 학술발표논문집
2018 .12
-
졸음 운전 감지 알고리즘 분석
한국정보과학회 학술발표논문집
2018 .06
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2017-530-001948590