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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.2
- 수록면
- 15 - 29 (15page)
- DOI
- 10.5143/JESK.2022.41.1.15
이용수
초록· 키워드
오류제보하기
Objective: This study developed an artificial neural network (ANN) model that can detect an early sign of drowsy driving (fighting-off drowsiness) based on electrocardiography (ECG) signals.
Background: Detecting an early state of drowsy driving is very important to prevent vehicle accidents on the road by providing appropriate interventions to the driver.
Method: The ECG signals for forty-three participants (mean age: 23.1, SD: 1.6) were recorded while performing a simulator-based monotonous driving for 20 minutes, and the ECG for twenty participants (mean age: 23.2, SD: 1.3) who suffered drowsiness were used in further analysis. The three driver states (normal, fighting-off drowsiness, and drowsy) were determined through participant"s subjective report and video recording analysis. Six ECG measures in time and frequency domains were derived from the ECG and pre-processed to compensate individual variations in heart response.
Results: The model was trained using a feedforward network with a scaled conjugate gradient, and its average accuracy was over 99% for the training and testing data.
Conclusion: This study showed that the ECG can be used as a biometric indicator for the detection of the driver"s drowsiness condition.
Application: The proposed model would be useful to the development of drowsiness detection system that can provide early warning to the driver at the onset of drowsiness.
Background: Detecting an early state of drowsy driving is very important to prevent vehicle accidents on the road by providing appropriate interventions to the driver.
Method: The ECG signals for forty-three participants (mean age: 23.1, SD: 1.6) were recorded while performing a simulator-based monotonous driving for 20 minutes, and the ECG for twenty participants (mean age: 23.2, SD: 1.3) who suffered drowsiness were used in further analysis. The three driver states (normal, fighting-off drowsiness, and drowsy) were determined through participant"s subjective report and video recording analysis. Six ECG measures in time and frequency domains were derived from the ECG and pre-processed to compensate individual variations in heart response.
Results: The model was trained using a feedforward network with a scaled conjugate gradient, and its average accuracy was over 99% for the training and testing data.
Conclusion: This study showed that the ECG can be used as a biometric indicator for the detection of the driver"s drowsiness condition.
Application: The proposed model would be useful to the development of drowsiness detection system that can provide early warning to the driver at the onset of drowsiness.
목차
1. Introduction
2. Method and Materials
3. Results
4. Discussion
5. Conclusion
References
참고문헌 (0)
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