1. Home
  2. Vol 8, No 12 (2022)
  3. Abdul Razak

Physiological-based Driver Monitoring Systems: A Scoping Review

Siti Fatimah Abdul Razak, Sumendra Yogarayan, Azlan Abdul Aziz, Mohd Fikri Azli Abdullah, Noor Hisham Kamis

Abstract


A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion.

 

Doi: 10.28991/CEJ-2022-08-12-020

Full Text: PDF


Keywords


Driver Monitoring System; ADAS; Vehicle Safety; Driving Behavior.

References


Guettas, A., Ayad, S., & Kazar, O. (2019). Driver State Monitoring System. Proceedings of the 4th International Conference on Big Data and Internet of Things, 28, 1-7. doi:10.1145/3372938.3372966.

Halin, A., Verly, J. G., & Van Droogenbroeck, M. (2021). Survey and synthesis of state of the art in driver monitoring. Sensors, 21(16), 5558. doi:10.3390/s21165558.

Lobo, A., Ferreira, S., & Couto, A. (2020). Exploring monitoring systems data for driver distraction and drowsiness research. Sensors (Switzerland), 20(14), 1–15. doi:10.3390/s20143836.

Manstetten, D., Beruscha, F., Bieg, H. J., Kobiela, F., Korthauer, A., Krautter, W., & Marberger, C. (2020). The Evolution of Driver Monitoring Systems: A Shortened Story on Past, Current and Future Approaches How Cars Acquire Knowledge About the Driver's State. 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services. doi:10.1145/3406324.3425896.

Meteier, Q., Capallera, M., Ruffieux, S., Angelini, L., Abou Khaled, O., Mugellini, E., Widmer, M., & Sonderegger, A. (2021). Classification of Drivers’ Workload Using Physiological Signals in Conditional Automation. Frontiers in Psychology, 12. doi:10.3389/fpsyg.2021.596038.

Castro, I. D., Mercuri, M., Patel, A., Puers, R., Van Hoof, C., & Torfs, T. (2019). Physiological driver monitoring using capacitively coupled and radar sensors. Applied Sciences (Switzerland), 9(19), 3994. doi:10.3390/app9193994.

Doudou, M. S., Bouabdallah, A., & Cherfaoui, V. (2018). A light on physiological sensors for efficient driver drowsiness detection system. Sensors & Transducers Journal, 224(8), 39-50.

Darzi, A., Gaweesh, S. M., Ahmed, M. M., & Novak, D. (2018). Identifying the causes of drivers’ hazardous states using driver characteristics, vehicle kinematics, and physiological measurements. Frontiers in Neuroscience, 12. doi:10.3389/fnins.2018.00568.

Chowdhury, A., Shankaran, R., Kavakli, M., & Haque, M. M. (2018). Sensor Applications and Physiological Features in Drivers’ Drowsiness Detection: A Review. IEEE Sensors Journal, 18(8), 3055–3067. doi:10.1109/JSEN.2018.2807245.

Arakawa, T. (2021). A review of heartbeat detection systems for automotive applications. Sensors, 21(18), 6112. doi:10.3390/s21186112.

Guo, K., Zhai, T., Purushothama, M. H., Dobre, A., Meah, S., Pashollari, E., Vaish, A., Dewilde, C., & Islam, M. N. (2022). Contactless Vital Sign Monitoring System for In-Vehicle Driver Monitoring Using a Near-Infrared Time-of-Flight Camera. Applied Sciences (Switzerland), 12(9). doi:10.3390/app12094416.

Radhakrishnan, V., Merat, N., Louw, T., Gonçalves, R. C., Torrao, G., Lyu, W., Puente Guillen, P., & Lenné, M. G. (2022). Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 87, 149–163. doi:10.1016/j.trf.2022.04.002.

Persson, A., Jonasson, H., Fredriksson, I., Wiklund, U., & Ahlstrom, C. (2021). Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3316–3325. doi:10.1109/TITS.2020.2981941.

Murugan, S., Selvaraj, J., & Sahayadhas, A. (2019). Analysis of Different Measures to Detect Driver States: A Review. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India. doi:10.1109/icscan.2019.8878844.

Warnecke, J. M., Ganapathy, N., Koch, E., Dietzel, A., Flormann, M., Henze, R., & Deserno, T. M. (2022). Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving. Sensors, 22(11), 4198. doi:10.3390/s22114198.

Wang, C. S., Huang, Y. C., Wang, T. W., & Lee, S. H. (2019). Monitor a driver behavior by ECG measurement. International Journal of Information and Education Technology, 9(3), 184–188. doi:10.18178/ijiet.2019.9.3.1196.

Balam, V. P., & Chinara, S. (2021). Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection. Physical and Engineering Sciences in Medicine, 44(3), 713-726. doi:10.1007/s13246-021-01020-3.

Balandong, R. P., Ahmad, R. F., Saad, M. N. M., & Malik, A. S. (2018). A review on EEG-based automatic sleepiness detection systems for driver. Ieee Access, 6, 22908-22919. doi:10.1109/ACCESS.2018.2811723.

Jiao, Y., Deng, Y., Luo, Y., & Lu, B. L. (2020). Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks. Neurocomputing, 408, 100–111. doi:10.1016/j.neucom.2019.05.108.

Bulagang, A. F., Weng, N. G., Mountstephens, J., & Teo, J. (2020). A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Informatics in Medicine Unlocked, 20, 100363. doi:10.1016/j.imu.2020.100363.

Rahman, N.A.A., Mustafa, M., Sulaiman, N., Samad, R., Abdullah, N.R.H. (2022). EMG Signal Segmentation to Predict Driver’s Vigilance State. Human-Centered Technology for a Better Tomorrow. Lecture Notes in Mechanical Engineering, Springer, Singapore. doi:10.1007/978-981-16-4115-2_3.

Hayawi, A. A., & Waleed, J. (2019). Driver's drowsiness monitoring and alarming auto-system based on EOG signals. 2019 2nd International Conference on Engineering Technology and its Applications (IICETA). doi:10.1109/IICETA47481.2019.9013000.

Bhardwaj, R., Parameswaran, S., & Balasubramanian, V. (2018). Comparison of Driver Fatigue Trend on simulator and on-road driving based on EMG correlation. 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). doi:10.1109/iciinfs.2018.8721431.

Rahman, H., Ahmed, M. U., & Begum, S. (2020). Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration. IEEE Transactions on Biomedical Engineering, 67(1), 88–98. doi:10.1109/TBME.2019.2908349.

Amidei, A., Fallica, P. G., Conoci, S., & Pavan, P. (2021). Validating Photoplethysmography (PPG) data for driver drowsiness detection. 2021 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), Bologna, Italy. doi:10.1109/metroautomotive50197.2021.9502865.

Sikander, G., & Anwar, S. (2019). Driver Fatigue Detection Systems: A Review. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2339–2352. doi:10.1109/TITS.2018.2868499.

Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology: Theory and Practice, 8(1), 19–32. doi:10.1080/1364557032000119616.

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. doi:10.7326/m18-0850.

Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan-a web and mobile app for systematic reviews. Systematic Reviews, 5(1). doi:10.1186/s13643-016-0384-4.

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. doi:10.1007/s11192-009-0146-3.

Choi, G., Lim, K., & Pan, S. B. (2022). Driver Identification System Using 2D ECG and EMG Based on Multistream CNN for Intelligent Vehicle. IEEE Sensors Letters, 6(6), 6001904. doi:10.1109/LSENS.2022.3175787.

Choi, G. H., Lim, K., & Pan, S. B. (2020). Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles. Sensors, 21(1), 202. doi:10.3390/s21010202.

Inalegwu, O. C., Maliki, D., Agajo, J., Ajao, L. A., & Abu, A. D. (2018). (2018). Fingerprint Based Driver’S Identification System. I-Manager’s Journal on Pattern Recognition, 5(2), 30. doi:10.26634/jpr.5.2.15730.

Lin, X., Zhang, K., Cao, W., & Zhang, L. (2018). Driver Evaluation and Identification Based on Driving Behavior Data. 2018 5th International Conference on Information Science and Control Engineering (ICISCE). doi:10.1109/icisce.2018.00154.

Ezzini, S., Berrada, I., & Ghogho, M. (2018). Who is behind the wheel? Driver identification and fingerprinting. Journal of Big Data, 5(1). doi:10.1186/s40537-018-0118-7.

Thanda Swe, K., & Kyaw, T. (2020). Fingerprint RFID Recognition System. International Journal of Creative and Innovative Research, 3(1), 21–25.

Santos, A., Medeiros, I., Resque, P., Rosário, D., Nogueira, M., Santos, A., Cerqueira, E., & Chowdhury, K. R. (2018). ECG-Based User Authentication and Identification Method on VANETs. Proceedings of the 10th Latin America Networking Conference. doi:10.1145/3277103.3277138.

Zero, E., Bersani, C., & Sacile, R. (2021). EEG Real Time Analysis for Driver's Arm Movements Identification. Sensors & Transducers, 251(4), 11-18.

Karumudi, B. R., Gist, P., Shahzad, M. Q., & Wagner, G. M. (2019). Biometric Authentication for Automobiles. Syracuse University, New York, United States.

Rundo, F., & Battiato, S. (2022). Deep Learning Sobriety Monitoring System in Road-driven Car Driving Risk Assessment Pipeline. 2022 5th International Conference on Circuits, Systems and Simulation, ICCSS 2022, 166–172. doi:10.1109/ICCSS55260.2022.9802349.

Vismaya, U. K., & Saritha, E. (2020). A Review on Driver Distraction Detection Methods. 2020 International Conference on Communication and Signal Processing (ICCSP). doi:10.1109/iccsp48568.2020.9182316.

Ghandour, R., Neji, B., El-Rifaie, A. M., & Al Barakeh, Z. (2020). Driver Distraction and Stress Detection Systems: A Review. International Journal of Engineering and Applied Sciences (IJEAS), 7(4), 39–46. doi:10.31873/ijeas.7.04.10.

Moslemi, N., Soryani, M., & Azmi, R. (2021). Computer vision-based recognition of driver distraction: A review. Concurrency and Computation: Practice and Experience, 33(24). doi:10.1002/cpe.6475.

Zheng, W. L., Gao, K., Li, G., Liu, W., Liu, C., Liu, J. Q., Wang, G., & Lu, B. L. (2020). Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment. IEEE Transactions on Intelligent Transportation Systems, 21(1), 170–184. doi:10.1109/TITS.2018.2889962.

Song, K., Zhou, L., & Wang, H. (2021). Deep coupling recurrent auto-encoder with multi-modal EEG and EOG for vigilance estimation. Entropy, 23(10), 1316. doi:10.3390/e23101316.

Gjoreski, M., Gams, M. Z., Luštrek, M., Genc, P., Garbas, J. U., & Hassan, T. (2020). Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions from Physiological and Visual Signals. IEEE Access, 8, 70590–70603. doi:10.1109/ACCESS.2020.2986810.

Li, P., Li, Y., Yao, Y., Wu, C., Nie, B., & Li, S. E. (2022). Sensitivity of Electrodermal Activity Features for Driver Arousal Measurement in Cognitive Load: The Application in Automated Driving Systems. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14954–14967. doi:10.1109/TITS.2021.3135266.

Kang, Z., & Yang, X. (2020). Fatigue Driving Characteristics Analysis Based on Data of Driver Monitoring System. CICTP 2020. doi:10.1061/9780784482933.370.

Laouz, H., Ayad, S., & Terrissa, L. S. (2020). Literature Review on Driver’s Drowsiness and Fatigue Detection. 2020 International Conference on Intelligent Systems and Computer Vision (ISCV). doi:10.1109/iscv49265.2020.9204306.

Gyulyev, N., Galkin, A., Schlosser, T., Capayova, S., Lobashov, O. (2022). Assessing Driver Fatigue during Urban Traffic Congestion Using ECG Method. Dynamics in Logistics. LDIC 2022. Lecture Notes in Logistics, Springer, Cham, Switzerland. doi:10.1007/978-3-031-05359-7_36.

Low, I., Molesworth, B. R. C., & Burgess, M. (2021). The fatiguing effect of broadband noise: An EEG-based study. Accident Analysis & Prevention, 151, 105901. doi:10.1016/j.aap.2020.105901.

Wang, H., Dragomir, A., Abbasi, N. I., Li, J., Thakor, N. V., & Bezerianos, A. (2018). A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive Neurodynamics, 12(4), 365–376. doi:10.1007/s11571-018-9481-5.

Liu, L., Ji, Y., Gao, Y., Ping, Z., Kuang, L., Li, T., & Xu, W. (2021). A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals. Journal of Healthcare Engineering, 2021. doi:10.1155/2021/7799793.

Jiang, Y., Zhang, Y., Lin, C., Wu, D., & Lin, C. T. (2021). EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1752–1764. doi:10.1109/TITS.2020.2973673.

Hu, J., & Min, J. (2018). Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model. Cognitive Neurodynamics, 12(4), 431–440. doi:10.1007/s11571-018-9485-1.

Sheykhivand, S., Rezaii, T. Y., Mousavi, Z., Meshgini, S., Makouei, S., Farzamnia, A., Danishvar, S., & Teo Tze Kin, K. (2022). Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network. Electronics (Switzerland), 11(14), 2169. doi:10.3390/electronics11142169.

Nemcova, A., Svozilova, V., Bucsuhazy, K., Smisek, R., Mezl, M., Hesko, B., Belak, M., Bilik, M., Maxera, P., Seitl, M., Dominik, T., Semela, M., Sucha, M., & Kolar, R. (2021). Multimodal Features for Detection of Driver Stress and Fatigue: Review. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3214–3233. doi:10.1109/TITS.2020.2977762.

Chaudhuri, A., & Routray, A. (2020). Driver Fatigue Detection through Chaotic Entropy Analysis of Cortical Sources Obtained from Scalp EEG Signals. IEEE Transactions on Intelligent Transportation Systems, 21(1), 185–198. doi:10.1109/TITS.2018.2890332.

Ren, Z., Li, R., Chen, B., Zhang, H., Ma, Y., Wang, C., Lin, Y., & Zhang, Y. (2021). EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function. Frontiers in Neurorobotics, 15. doi:10.3389/fnbot.2021.618408.

Gaio, A., & Cugurullo, F. (2022). Cyclists and autonomous vehicles at odds: Can the Transport Oppression Cycle be Broken in the Era of Artificial Intelligence?. AI & Society, 1-15. doi:10.1007/s00146-022-01538-4.

Dong, N., Li, Y., Gao, Z., Ip, W. H., & Yung, K. L. (2019). A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system. IEEE Access, 7, 124702–124711. doi:10.1109/ACCESS.2019.2937914.

Lu, J., Zheng, X., Tang, L., Zhang, T., Sheng, Q. Z., Wang, C., Jin, J., Yu, S., & Zhou, W. (2021). Can Steering Wheel Detect Your Driving Fatigue? IEEE Transactions on Vehicular Technology, 70(6), 5537–5550. doi:10.1109/tvt.2021.3072936.

Wörle, J., Metz, B., Thiele, C., & Weller, G. (2019). Detecting sleep in drivers during highly automated driving: The potential of physiological parameters. IET Intelligent Transport Systems, 13(8), 1241–1248. doi:10.1049/iet-its.2018.5529.

Buendia, R., Forcolin, F., Karlsson, J., Arne Sjöqvist, B., Anund, A., & Candefjord, S. (2019). Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness. Traffic Injury Prevention, 20(3), 249–254. doi:10.1080/15389588.2018.1548766.

Lu, K., Karlsson, J., Dahlman, A. S., Sjoqvist, B. A., & Candefjord, S. (2022). Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4801–4810. doi:10.1109/TITS.2021.3127944.

Fujiwara, K., Abe, E., Kamata, K., Nakayama, C., Suzuki, Y., Yamakawa, T., Hiraoka, T., Kano, M., Sumi, Y., Masuda, F., Matsuo, M., & Kadotani, H. (2019). Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation with EEG. IEEE Transactions on Biomedical Engineering, 66(6), 1769–1778. doi:10.1109/TBME.2018.2879346.

Antunes, A. R., Meneses, M. V. P. R., Goncalves, J., & Braga, A. C. (2022). An Intelligent System to Detect Drowsiness at the Wheel. 2022 10th International Symposium on Digital Forensics and Security (ISDFS). doi:10.1109/isdfs55398.2022.9800836.

Chen, H., & Chen, L. (2017). Support vector machine classification of drunk driving behaviour. International Journal of Environmental Research and Public Health, 14(1). doi:10.3390/ijerph14010108.

Barua, S., Ahmed, M. U., Ahlström, C., & Begum, S. (2019). Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Systems with Applications, 115, 121–135. doi:10.1016/j.eswa.2018.07.054.

Chen, J., Wang, H., & Hua, C. (2018). Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks. International Journal of Psychophysiology, 133, 120–130. doi:10.1016/j.ijpsycho.2018.07.476.

Rachakonda, L., Mohanty, S. P., Kougianos, E., & Sayeed, M. A. (2020). Smart-Steering: An IoMT-Device to Monitor Blood Alcohol Concentration using Physiological Signals. 2020 IEEE International Conference on Consumer Electronics (ICCE). doi:10.1109/icce46568.2020.9043045.

Subramaniyam, M., Kim, S. E., Min, S. N., Lee, H., Hong, S. H., & Park, S. J. (2018). Study of effects of blood alcohol consumption (BAC) level on drivers physiological behavior and driving performance under simulated environment. International Journal of Engineering and Technology (UAE), 7(2), 86–91. doi:10.14419/ijet.v7i2.8.10336.

Xing, Q., Chen, Z., Zhang, Z., Wang, R., & Zhang, T. (2021). Modelling driving and charging behaviours of electric vehicles using a data-driven approach combined with behavioural economics theory. Journal of Cleaner Production, 324, 129243. doi:10.1016/j.jclepro.2021.129243.

Sanguansri, P., Apiwong-Ngam, N., Ngamjarurojana, A., & Choopun, S. (2022). Development of non-invasive alcohol analyzer using Photoplethysmographytle. Journal of Physics: Conference Series, 2145(1). doi:10.1088/1742-6596/2145/1/012059.

Wang, W. F., Yang, C. Y., & Wu, Y. F. (2018). SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring. Personal and Ubiquitous Computing, 22(2), 275–287. doi:10.1007/s00779-017-1042-0.

Evin, M., Taillard, J., De la Fuente, H. L., Galy, E., & Berthelon, C. (2018). Detection of functional state after alcohol consumption by classification and machine learning technics. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2237-2242. doi:10.1109/BIBM.2018.8621310.

Chen, Y. Y., Lin, C. L., Lin, Y. C., & Zhao, C. (2018). Non-invasive detection of alcohol concentration based on photoplethysmogram signals. IET Image Processing, 12(2), 188–193. doi:10.1049/iet-ipr.2017.0625.

Chhabra, G., Sapra, V., Sharma, R., Bansal, R., Joshi, M., & Joshi, K. (2021). Design Engineering Eye State Classification Based on EEG Signals. Design Engineering, 6, 2544–2551.

Won Park, S., won Choi, J., Hyun Kim, T., Hun Seo, J., Gyu Jeong, M., In Lee, K., & Sung Kim, H. (2022). Prediction of Alcohol Consumption Based on Biosignals and Assessment of Driving Ability According to Alcohol Consumption. Journal of Biomedical Engineering Research, 43, 27–34. doi:10.9718/JBER.2022.43.1.27.

Ye, Y., Ma, L., Liu, J., Zhang, Z., Gu, C., & Mao, J. F. (2022). A Novel Non-Contact Drunkenness Monitoring Technique Based on A 24-GHz Interferometric Radar System. 2022 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2022, 296–298. doi:10.1109/IMBioC52515.2022.9790121.


Full Text: PDF

DOI: 10.28991/CEJ-2022-08-12-020

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2023 Siti Fatimah Abdul Razak, Sumendra Yogarayan, Azlan Abdul Aziz, Mohd Fikri Azli Abdullah, Noor Hisham Kamis

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message