Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis and Processing of Driver's Biological Signal of Workload

작업 부하에 따른 운전자의 생체신호 처리 및 특성 분석

  • 허윤석 (계명대학교 의용공학과) ;
  • 이재천 (계명대학교 동산의료원 심장내과) ;
  • 김윤년 (계명대학교 기계자동차공학과)
  • Received : 2015.06.18
  • Accepted : 2015.07.01
  • Published : 2015.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

졸음 운전 등 운전자의 상태 변화에 따른 자동차 사고가 급증하고 있으며 이를 방지하기 위한 시스템 구축 및 운전자의 상태를 판단하는 알고리즘 개발이 요구되어 지고 있다. 본 논문에서는 모의 주행 시스템을 통한 운전자의 심박변이도, 산호 포화도 (SPO2), 체온을 측정하여 운전자의 상태를 알려 주는 실험을 수행하였다. 즉, 심박변이도 (Heart rate variability, HRV) 분석을 위해 운전자의 심전도(Electrocardiogram, ECG) 신호를 획득 한 후 심전도 P,QRS, T 파형 중 R peak 을 자동으로 검출하였고 이를 통해 구한 R-R interval을 이용하여 HRV의 주요 파라메타를 시간영역(time domain)으로 해석하여 작업 환경에 따른 운전자의 상태를 비교 분석하였다.

Keywords

References

  1. Traffic Accident Analysis Center, "Traffic Accident Statistical Analysis 2012", KOROAD, 2012.
  2. F. Jurysta, P. Bome, P. Miquotte, M Dumont, J. Lanquart, J. Deqaute, P Linkowski, "A study of the dynamic interactions bwtween sleep EEG and heart rate varability in healthy young men," Clin. Neurophysiol., Vol. 114, pp. 2146-2155, 2003. https://doi.org/10.1016/S1388-2457(03)00215-3
  3. W. Kim, K, Kim, S. Park, J. Shin and Y. Yoon, "Analyzing Heart Rate Variability for Automatic Sleep Stage Classification," Korean J. of the Science of Emo. and sens.,Vol. 6, No. 4, pp.9-14., 2003.
  4. N. Egelund, "Spectral analysis of heart rate variability as an indicator of driver fatigue. Ergonomics," Vol. 25, pp. 663-672, 1982. https://doi.org/10.1080/00140138208925026
  5. J. Aasman, G. Mulder, L. Mulder "Operator effort and the measurement of heart rate variability," Human Factors, Vol. 29, pp. 161-170, 1987. https://doi.org/10.1177/001872088702900204
  6. P. Jorna, "Heart rate and workload variation in actual and simulated flight," Ergonomics, Vol 36, No. 9, pp. 1043-1054, 1993. https://doi.org/10.1080/00140139308967976
  7. Reed, M.P., Green, P.A. Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialing task. Ergonomics, Vol. 42, pp. 1015-1037, 1999. https://doi.org/10.1080/001401399185117
  8. L. Gang and W. Chung, "Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier," Sensors, Vol. 13, pp. 16495-16511, 2013.
  9. A. Karel, Brookhuis, W. Dick, "Monitoring drivers' mental workload in driving simulators using physiological measures," Accident Analysis and Prevention, Vol. 42, pp. 898-903, 2010. https://doi.org/10.1016/j.aap.2009.06.001
  10. S. Deboleena, M. Madhuchhanda, "R-peak detection algorithm for ECG using double difference and RR interval processing," Procedia Technology, Vol. 4, pp. 873-877, 2012. https://doi.org/10.1016/j.protcy.2012.05.143
  11. M.S Kim, Y.N. Kim, and Y.S. Heo, "Characteristics of heart rate variability derived from ECG during the driver's wake and sleep status", Transactions of KSAE, Vol. 22, No.3, pp.136-142, 2014. https://doi.org/10.7467/KSAE.2014.22.3.136

Cited by

  1. Electrocardiographic characteristics of significant factors of detected atrial fibrillation using WEMS vol.20, pp.6, 2015, https://doi.org/10.9723/jksiis.2015.20.6.037
  2. Development of a Classification Model for Driver's Drowsiness and Waking Status Using Heart Rate Variability and Respiratory Features vol.35, pp.5, 2016, https://doi.org/10.5143/JESK.2016.35.5.371