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
- 2020.12
- 수록면
- 625 - 636 (12page)
- DOI
- 10.5143/JESK.2020.39.6.625
이용수
이 논문의 연구 히스토리 (3)
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
Objective: This study developed a real-time system to detect driver"s cognitive load using a multi-layer artificial neural network (MANN) based on electrocardiography (ECG) signals. The real-time system was aimed at classifying driver"s status into either normal or overload.
Background: Driving with cognitive load is considered as one of significant factors for traffic accidents. Thus, an early detection of this risky status while driving is needed to prevent vehicle accidents.
Method: The ECG signals of this study were measured from 22 participants who performed simulator-based driving experiment under two different conditions (1: normal driving, 2: overload driving (driving while doing a two-back task or an arithmetic task)). A real-time detection system was developed using MANN on the ECG signals and its effectiveness was evaluated for two new participants who drove under the two driving conditions.
Results: The MANN model used for the real-time detection system showed perfect accuracy (100%), sensitivity (100%), and specificity (100%) for both of the training and testing data sets. In addition, the proposed real-time detection system successfully detected the change of participant"s status with a reasonable time delay (mean = 4.5 seconds).
Conclusion: This study demonstrated that the ECG signals can be used as a biometric measure for the detection of the driver"s cognitive status in real-time.
Application: The proposed detection system would be useful for the development of an intelligent vehicle that can provide timely interventions and/or warnings at the early onset of cognitive overload.
Background: Driving with cognitive load is considered as one of significant factors for traffic accidents. Thus, an early detection of this risky status while driving is needed to prevent vehicle accidents.
Method: The ECG signals of this study were measured from 22 participants who performed simulator-based driving experiment under two different conditions (1: normal driving, 2: overload driving (driving while doing a two-back task or an arithmetic task)). A real-time detection system was developed using MANN on the ECG signals and its effectiveness was evaluated for two new participants who drove under the two driving conditions.
Results: The MANN model used for the real-time detection system showed perfect accuracy (100%), sensitivity (100%), and specificity (100%) for both of the training and testing data sets. In addition, the proposed real-time detection system successfully detected the change of participant"s status with a reasonable time delay (mean = 4.5 seconds).
Conclusion: This study demonstrated that the ECG signals can be used as a biometric measure for the detection of the driver"s cognitive status in real-time.
Application: The proposed detection system would be useful for the development of an intelligent vehicle that can provide timely interventions and/or warnings at the early onset of cognitive overload.
목차
1. Introduction
2. Development of the Real-Time Detection System
3. Performance Evaluation of the Real-Time Detection System
4. Discussion
5. Conclusion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Development of Real-time Assessment System of Driver’s Cognitive Workload Using Artificial Neural Network on ECG
대한인간공학회 학술대회논문집
2017 .04
-
Classification of Driver States using Artificial Neural Network on ECG Signals
대한인간공학회 학술대회논문집
2019 .10
-
Classification of Driver’s Cognitive Workload Levels using Artificial Neural Network on ECG
대한산업공학회 추계학술대회 논문집
2015 .11
-
Detecting Early Signs of Drowsy Driving using an Artificial Neural Network on Electrocardiography Signals
대한인간공학회지
2022 .02
-
인지부하 기전과 평가방법에 대한 고찰
대한인간공학회지
2018 .06
-
인지 상태 변화 분석을 위한 ECG 지표 개발 및 효용성 검증
대한인간공학회 학술대회논문집
2019 .10
-
운전자 상태 판단을 위한 무자각 생체 인식 모듈에 관한 연구
한국자동차공학회 춘계학술대회
2015 .05
-
딥러닝 기반의 운전자의 안전/위험 상태 인지 시스템 개발
로봇학회 논문지
2018 .03
-
고령 운전자 첨단 운전 보조장치 도입 필요성에 관한 연구
대한전자공학회 학술대회
2024 .06
-
An Automatic Cardiac Arrhythmia Classification System Based on ECG Signal Processing using 1-D Convolutional Neural Network (CNN)
대한기계학회 춘추학술대회
2021 .04
-
얼굴 검출 기반 상태 분석을 통한 운전자 상태 감시 시스템
대한전자공학회 학술대회
2021 .06
-
Real -Time ECG Signal Acquisition and Processing Using LabVIEW
센서학회지
2020 .01
-
A Simple and Robustness Algorithm for ECG R- peak Detection
Journal of Electrical Engineering & Technology
2018 .09
-
System Architecture for Early Sleep Detection and Warning System for Driver’s Safety
한국통신학회 학술대회논문집
2017 .06
-
딥러닝 기술을 활용한 차량 운전자의 심전도 기반 스트레스 감지
한국소음진동공학회논문집
2025 .04
-
TFT를 이용한 ECG 신호예측기반 실시간 이상탐지 시스템 구현
한국통신학회 학술대회논문집
2023 .06
-
Development of Internal Monitoring Platform for collecting driver’s condition information based on Images and physical signals
한국자동차공학회 추계학술대회 및 전시회
2017 .11
-
Effects of Driver Drowsiness Levels while Simulated Driving on ECG Signals
대한인간공학회 학술대회논문집
2018 .11
-
카메라 활용 운전자 부주의 판단 시스템 연구
한국자동차공학회 추계학술대회 및 전시회
2018 .11
-
ECG Identification with One Dimensional Convolutional Neural Network
한국통신학회 학술대회논문집
2021 .02
이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2021-530-001486807