The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

A Study On The Classification Of Driver's Sleep State While Driving Through BCG Signal Optimization

BCG 신호 최적화를 통한 주행중 운전자 수면 상태 분류에 관한 연구

  • 박진수 (한국전자기술연구원 스마트가전혁신지원센터) ;
  • 정지성 (한국전자기술연구원 스마트가전혁신지원센터) ;
  • 양철승 (한국전자기술연구원 스마트가전혁신지원센터) ;
  • 이정기 (한국전자기술연구원 스마트가전혁신지원센터)
  • Received : 2022.10.21
  • Accepted : 2022.11.08
  • Published : 2022.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

졸음운전은 교통사고 발생률을 높이고 사망사고로 이어지기 때문에 많은 사회적 관심이 필요하다. 졸음운전으로 인한 사고 건수는 매년 증가하고 있다. 따라서 전 세계적으로 이 문제를 해결하기 위해 다양한 생체신호 측정을 위한 연구가 수행되고 있다. 본 논문에서는 그 중에 비접촉 방식의 생체신호 분석에 중점을 두고 있다. 주행중인 차량에서는 엔진, 타이어, 차체 진동 등 다양한 노이즈가 발생한다. 압전센서로 주행중인 차량에서 운전자의 심박수와 호흡수를 측정하기 위해 차량 진동을 완충할 수 있는 센서 플레이트를 설계했고 차량에서 발생하는 노이즈를 줄일 수 있었다. 또한 압전센서의 신호 기반 CNN-LSTM 앙상블 학습 기법으로 모델을 추출하여 운전자가 수면중인지 아닌지 분류하는 시스템을 개발했다. 수면 상태를 학습시키기 위해 30초마다 피험자의 생체 신호를 획득하였고, 797개의 데이터를 비교 분석하였다.

Keywords

Acknowledgement

본 연구는 국토교통부의 국토교통기술사업화지원사업의 연구비지원에 의해 수행되었음. [과제명 : 비접촉 생체정보 측정기능이 포함된 스마트 디퓨저 기반 거주자 맞춤형 Home-HAS(Health, Air, Safety) 서비스 개발] [과제번호 : 21TBIP-C161696-01]

References

  1. IEA, "Global EV Outlook 2021: Accelerating ambitions despite the pandemic"(www.iea.org)
  2. Y. Sim, S. J. Moon, & J. Y. Lee, "A Study on Korean Sentiment Analysis Rate Using Neural Network and Ensemble Combination", International Journal of Advanced Culture Technology, Vol. 9, No. 4, pp 268-273, 2021. https://doi.org/10.17703/IJACT.2021.9.4.268
  3. Srivathsa, C. R., Dhanasekhar, S., & Trilok, J., "IOT Based Smart Solutions For EV (Doctoral dissertation", CMR Institute of Technology, Bangalore), 2020.
  4. Rahim, M. A., Rahman, M. A., Rahman, M. M., Asyhari, A. T., Bhuiyan, M. Z. A., & Ramasamy, D., "Evolution of IoT-enabled connectivity and applications in automotive industry: A review", Vehicular Communications, Vol. 27, pp 100285, 2021. DOI:10.1016/j.vehcom.2020.100285
  5. World Health Organization, "Global status report on road safety 2018" (www.who.int)
  6. Chowdhury, M. E., El Beheri, S. H., Albardawil, M. N., Moustafa, A. K. M. N., Halabi, O., & Kiranyaz, M. S., "Driver drowsiness detection study using heart rate variability analysis in virtual reality environment", In Qatar Foundation Annual Research Conference Proceedings Volume Issue 3, Vol. 2018, No. 3, pp ICTPD1132., 2018.
  7. Vicente, J., Laguna, P., Bartra, A., & Bailon, R., "Drowsiness detection using heart rate variability", Medical & biological engineering & computing, Vol. 54, No. 6, pp 927-937, 2016. https://doi.org/10.1007/s11517-015-1448-7
  8. Ramzan, M., Khan, H. U., Awan, S. M., Ismail, A., Ilyas, M., & Mahmood, A., "A survey on state-of-the-art drowsiness detection techniques", IEEE Access, Vol. 7, pp 61904-61919, 2019. DOI: 10.1109/access.2019.2914373
  9. Yun, Y., Lee, J., Kim, J., & Kim, Y., "Detection scheme of heart and respiration signals for a driver of car with a doppler radar", Journal of the Society of Disaster Information, Vol. 16, No. 1, pp 87-95, 2020. https://doi.org/10.15683/KOSDI.2020.3.31.087
  10. Min J. H., Lee J. W., Kim K. H., "A Basic Study on Realtime Estimating Respiration of Ballistocardiogram in Non-Invasive Way Using Finite Impulse Response Filter", The Transactions of the Korean Institute of Electrical Engineers (KIEE), Vol. 68, No. 7, pp 879-883, 2019. https://doi.org/10.5370/kiee.2019.68.7.879
  11. Yang, C., Ku, G. W., Lee, J. G., & Kim, K., "Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm", Journal of Electrical Engineering & Technology, Vol. 16, No. 2, pp 1043-1050, 2021. https://doi.org/10.1007/s42835-020-00601-8
  12. Achten, H., & Rojer, G., "Heart rate analysis using BCG: Determining the heart rate with an under the mattress sensor", Delft University of Technology, 2020.
  13. Ma, Y., Tian, F., Zhao, Q., & Hu, B., "Design and application of mental fatigue detection system using non-contact ECG and BCG measurement", In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1508-1513, 2018.
  14. Hafiz, M. A., Hashem, A. M., Khan, A. A. S., Rashid, M. H., & Faruqui, M. A. K., "Implementation of non-contact bed embedded ballistocardiogram signal measurement and valvular disease detection from this BCG signal", International Journal of Medical Engineering and Informatics, Vol. 13, No. 4, pp 289-296, 2021. https://doi.org/10.1504/IJMEI.2021.115970
  15. Janjua, G., Guldenring, D., Finlay, D., & McLaughlin, J., "Wireless chest wearable vital sign monitoring platform for hypertension", In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 821-824, 2017.
  16. Guo, Y., & Zhang, J., "Shock absorbing characteristics and vibration transmissibility of honeycomb paperboard", Shock and Vibration, Vol. 11, No. 5-6, pp 521-531, 2004. https://doi.org/10.1155/2004/936804
  17. Yang, C., Ku, G. W., Lee, J. G., & Kim, K., "Improving the Accuracy of Biosignal Analysis Using BCG by Applying a Signal-to-Noise Ratio and Similarity-Based Channel Selection Algorithm", Journal of Electrical Engineering & Technology 16.2, p.1043-1050, 2021. https://doi.org/10.1007/s42835-020-00601-8
  18. Van Hal, B., Rhodes, S., Dunne, B., & Bossemeyer, R., "Low-cost EEG-based sleep detection", In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 4571-4574, 2014.
  19. Lal, S. K., Craig, A., Boord, P., Kirkup, L., & Nguyen, H., "Development of an algorithm for an EEG-based driver fatigue countermeasure", Journal of safety Research, Vol. 34, No. 3, pp 321-328, 2003., DOI:10.1016/S0022-4375(03)00027-6
  20. Kweon, Y. S., Kwak, H. G., Shin, G. H., & Lee, M., "Automatic micro-sleep detection under car-driving simulation environment using nightsleep EEG", In 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1-6, 2021.
  21. Dreem Deadband (https://dreem.com/)
  22. Choi Se Jin, "A Method for accelerating training of Convolutional Neural Network", The Journal of the Convergence on Culture Technology, Vol. 3, no.4, pp. 171-175, 2017. DOI:10.17703/JCCT.2017.3.4.171
  23. YuJeong Sim, Seok-Jae Moon, Jong-Youg Lee "A Study on Korean Sentiment Analysis Rate Using Neural Network and Ensemble Combination", International Journal of Advanced Culture Technology, Vol.9 No.4 , pp.268, 2021. https://doi.org/10.17703/IJACT.2021.9.4.268