{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T18:13:26Z","timestamp":1786212806488,"version":"3.56.0"},"reference-count":136,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.<\/jats:p>","DOI":"10.3390\/s22052069","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:50:53Z","timestamp":1646790653000},"page":"2069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":221,"title":["A Review of Recent Developments in Driver Drowsiness Detection Systems"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-5566","authenticated-orcid":false,"given":"Yaman","family":"Albadawi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9785-3920","authenticated-orcid":false,"given":"Maen","family":"Takruri","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Communications Engineering, American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Awad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"ref_1","unstructured":"National Highway Traffic Safety Administration (2021, May 10). Drowsy Driving, Available online: https:\/\/www.nhtsa.gov\/risky-driving\/drowsy-driving."},{"key":"ref_2","unstructured":"Tefft, B.C. (2014). Prevalence of Motor Vehicle Crashes Involving Drowsy Drivers, United States, 2009\u20132013, Citeseer."},{"key":"ref_3","unstructured":"National Institutes of Health (2021, May 10). Drowsiness, Available online: https:\/\/medlineplus.gov\/ency\/article\/003208.htm#:~:text=Drowsiness%20refers%20to%20feeling%20abnormally,situations%20or%20at%20inappropriate%20times."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arakawa, T. (2021). Trends and future prospects of the drowsiness detection and estimation technology. Sensors, 21.","DOI":"10.3390\/s21237921"},{"key":"ref_5","unstructured":"National Safety Council (2021, May 10). Drivers are Falling Asleep Behind the Wheel. Available online: https:\/\/www.nsc.org\/road-safety\/safety-topics\/fatigued-driving."},{"key":"ref_6","unstructured":"National Sleep Foundation (2021, May 10). Drowsy Driving. Available online: https:\/\/www.sleepfoundation.org\/articles\/drowsy-driving."},{"key":"ref_7","first-page":"816","article-title":"A survey on drivers drowsiness detection techniques","volume":"1","author":"Fuletra","year":"2013","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pratama, B.G., Ardiyanto, I., and Adji, T.B. (2017, January 25\u201326). A review on driver drowsiness based on image, bio-signal, and driver behavior. Proceedings of the 2017 3rd International Conference on Science and Technology-Computer (ICST), Bandung, Indonesia.","DOI":"10.1109\/ICSTC.2017.8011855"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61904","DOI":"10.1109\/ACCESS.2019.2914373","article-title":"A survey on state-of-the-art drowsiness detection techniques","volume":"7","author":"Ramzan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1109\/TITS.2018.2868499","article-title":"Driver fatigue detection systems: A review","volume":"20","author":"Sikander","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ukwuoma, C.C., and Bo, C. (2019, January 11\u201313). Deep Learning Review on Drivers Drowsiness Detection. Proceedings of the 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), Bangkok, Thailand.","DOI":"10.1109\/TIMES-iCON47539.2019.9024642"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TITS.2010.2092770","article-title":"Driver inattention monitoring system for intelligent vehicles: A review","volume":"12","author":"Dong","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trf.2006.03.003","article-title":"Sleepy at the wheel: Knowledge, symptoms and behaviour among car drivers","volume":"10","author":"Nordbakke","year":"2007","journal-title":"Transportation Research Part F: Traffic Psychology and Behaviour"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/MCE.2015.2463373","article-title":"Detecting Driver Drowsiness: A survey of system designs and technology","volume":"4","year":"2015","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_15","unstructured":"Beirness, D.J., Simpson, H.M., Desmond, K., and The Road Safety Monitor 2004: Drowsy Driving (2022, March 02). Drowsy Driving. Available online: http:\/\/worldcat.org\/isbn\/0920071473."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.neucom.2019.02.014","article-title":"Driver\u2019s fatigue recognition based on yawn detection in thermal images","volume":"338","author":"Knapik","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Qian, J., Yao, Z., Jiao, X., and Pan, J. (2019). Convolutional two-stream network using multi-facial feature fusion for driver fatigue detection. Future Internet, 11.","DOI":"10.3390\/fi11050115"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8851485","article-title":"A fatigue driving detection algorithm based on facial motion information entropy","volume":"2020","author":"You","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mittal, A., Kumar, K., Dhamija, S., and Kaur, M. (2016, January 17\u201318). Head movement-based driver drowsiness detection: A review of state-of-art techniques. Proceedings of the 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India.","DOI":"10.1109\/ICETECH.2016.7569378"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.physbeh.2005.02.021","article-title":"Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers","volume":"84","author":"Otmani","year":"2005","journal-title":"Physiol. Behav."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1016\/j.clinph.2006.03.011","article-title":"Validation of the Karolinska sleepiness scale against performance and EEG variables","volume":"117","author":"Kaida","year":"2006","journal-title":"Clin. Neurophysiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shahid, A., Wilkinson, K., Marcu, S., and Shapiro, C.M. (2011). Karolinska sleepiness scale (KSS). STOP, THAT and One Hundred Other Sleep Scales, Springer.","DOI":"10.1007\/978-1-4419-9893-4_47"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/0001-4575(94)90019-1","article-title":"Evaluation of driver drowsiness by trained raters","volume":"26","author":"Wierwille","year":"1994","journal-title":"Accid. Anal. Prev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/THMS.2016.2549032","article-title":"Driver assistance system with a dual control scheme: Effectiveness of identifying driver drowsiness and preventing lane departure accidents","volume":"46","author":"Saito","year":"2016","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1109\/JSEN.2019.2960158","article-title":"Comprehensive Drowsiness Level Detection Model Combining Multimodal Information","volume":"20","author":"Sunagawa","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_26","unstructured":"Machine Learning Crash Course (2022, March 02). Classification: Accuracy. Available online: https:\/\/developers.google.com\/machine-learning\/crash-course\/classification\/accuracy."},{"key":"ref_27","unstructured":"Machine Learning Crash Course (2022, March 02). Classification: Precision and Recall. Available online: https:\/\/developers.google.com\/machine-learning\/crash-course\/classification\/precision-and-recall."},{"key":"ref_28","unstructured":"Leng, L.B., Giin, L.B., and Chung, W.-Y. (2015, January 1\u20134). Wearable driver drowsiness detection system based on biomedical and motion sensors. Proceedings of the 2015 IEEE SENSORS, Busan, Korea."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5119","DOI":"10.1109\/JSEN.2019.2904222","article-title":"A hybrid scheme for drowsiness detection using wearable sensors","volume":"19","author":"Mehreen","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_30","first-page":"549","article-title":"Non-intrusive driver drowsiness detection based on face and eye tracking","volume":"10","author":"Bamidele","year":"2019","journal-title":"Int J. Adv. Comput. Sci. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1167\/12.9.546","article-title":"Perclos threshold for drowsiness detection during real driving","volume":"12","author":"Lin","year":"2012","journal-title":"J. Vis."},{"key":"ref_32","unstructured":"Rosebrock, A. (2021, September 20). Eyeblink Detection with OpenCV, Python, and dlib. Available online: https:\/\/www.pyimagesearch.com\/2017\/04\/24\/eye-blink-detection-opencv-python-dlib\/_mjzu4CFQAAAAAdAAAAABAK."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Moujahid, A., Dornaika, F., Arganda-Carreras, I., and Reta, J. (2021). Efficient and compact face descriptor for driver drowsiness detection. Expert Syst. Appl., 168.","DOI":"10.1016\/j.eswa.2020.114334"},{"key":"ref_34","unstructured":"Popieul, J.C., Simon, P., and Loslever, P. (2003, January 9\u201311). Using driver\u2019s head movements evolution as a drowsiness indicator. Proceedings of the IEEE IV2003 Intelligent Vehicles Symposium, Columbus, OH, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Weng, C.-H., Lai, Y.-H., and Lai, S.-H. (2016, January 20\u201324). Driver drowsiness detection via a hierarchical temporal deep belief network. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54526-4_9"},{"key":"ref_36","unstructured":"Knapik, M. (2018). Thermal Images Database, GitHub. Available online: https:\/\/github.com\/mat02\/ThermalImagingInCar."},{"key":"ref_37","unstructured":"Wikipedia (2021, September 20). Voxel. Available online: https:\/\/en.wikipedia.org\/wiki\/Voxel."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17793","DOI":"10.1007\/s11042-020-08696-x","article-title":"Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator","volume":"79","author":"Kiashari","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","first-page":"1","article-title":"Smart real-time video surveillance platform for drowsiness detection based on eyelid closure","volume":"2019","author":"Anwar","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1016\/j.patcog.2014.03.024","article-title":"Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients","volume":"47","author":"Song","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ouabida, E., Essadike, A., and Bouzid, A. (2020). Optical correlator based algorithm for driver drowsiness detection. Optik, 204.","DOI":"10.1016\/j.ijleo.2019.164102"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TIT.1964.1053650","article-title":"Signal detection by complex spatial filtering","volume":"10","author":"Lugt","year":"1964","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ijleo.2017.04.012","article-title":"Misalignment influence of components on the performance of an integrated zigzag Vander Lugt correlator","volume":"140","author":"Xu","year":"2017","journal-title":"Optik"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.imavis.2009.11.005","article-title":"A new ranking method for principal components analysis and its application to face image analysis","volume":"28","author":"Thomaz","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_45","unstructured":"Gourier, N., Hall, D., and Crowley, J.L. (2004, January 22). Estimating face orientation from robust detection of salient facial features. Proceedings of the ICPR International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jesorsky, O., Kirchberg, K.J., and Frischholz, R.W. (2001, January 6\u20138). Robust face detection using the hausdorff distance. Proceedings of the International conference on audio-and video-based biometric person authentication, Halmstad, Sweden.","DOI":"10.1007\/3-540-45344-X_14"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2501643.2501647","article-title":"Hybrid method based on topography for robust detection of iris center and eye corners","volume":"9","author":"Villanueva","year":"2013","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_48","unstructured":"SHRP2: Transportation Research Board of the National Academies of Science (2021, September 20). The 2nd Strategic Highway Research Program Naturalistic Driving Study Dataset, Available online: https:\/\/insight.shrp2nds.us\/."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Maior, C.B.S., das Chagas Moura, M.J., Santana, J.M.M., and Lins, I.D. (2020). Real-time classification for autonomous drowsiness detection using eye aspect ratio. Expert Syst. Appl., 158.","DOI":"10.1016\/j.eswa.2020.113505"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-020-00306-9","article-title":"Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network","volume":"1","author":"Hashemi","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/0361198119847985","article-title":"Non-intrusive detection of drowsy driving based on eye tracking data","volume":"2673","author":"Zandi","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Celecia, A., Figueiredo, K., Vellasco, M., and Gonz\u00e1lez, R. (2020). A portable fuzzy driver drowsiness estimation system. Sensors, 20.","DOI":"10.3390\/s20154093"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., and Pantic, M. (2013, January 2\u20138). 300 faces in-the-wild challenge: The first facial landmark localization challenge. Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.59"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Alioua, N., Amine, A., Rziza, M., and Aboutajdine, D. (2011, January 29\u201331). Driver\u2019s fatigue and drowsiness detection to reduce traffic accidents on road. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Seville, Spain.","DOI":"10.1007\/978-3-642-23678-5_47"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Khunpisuth, O., Chotchinasri, T., Koschakosai, V., and Hnoohom, N. (December, January 28). Driver drowsiness detection using eye-closeness detection. Proceedings of the 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy.","DOI":"10.1109\/SITIS.2016.110"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"118727","DOI":"10.1109\/ACCESS.2019.2936663","article-title":"Real-time driver-drowsiness detection system using facial features","volume":"7","author":"Deng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., and Tang, X. (2015, January 7\u201313). Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., and Hariri, B. (2014, January 19). YawDD: A yawning detection dataset. Proceedings of the 5th ACM Multimedia Systems Conference, Singapore.","DOI":"10.1145\/2557642.2563678"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1007\/s00521-020-05209-7","article-title":"Deep CNN models-based ensemble approach to driver drowsiness detection","volume":"33","author":"Dua","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_60","first-page":"1","article-title":"Ensemble learning","volume":"11","author":"Sewell","year":"2011","journal-title":"Res. Note"},{"key":"ref_61","unstructured":"Rosebrock, A. (2021, September 20). Softmax Classifiers Explained. Available online: https:\/\/www.pyimagesearch.com\/2016\/09\/12\/softmax-classifiers-explained\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1992","DOI":"10.1364\/OL.35.001992","article-title":"Generic gamma correction for accuracy enhancement in fringe-projection profilometry","volume":"35","author":"Hoang","year":"2010","journal-title":"Opt. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4206","DOI":"10.1109\/TITS.2018.2883823","article-title":"Driver drowsiness detection using condition-adaptive representation learning framework","volume":"20","author":"Yu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tuzel, O., Porikli, F., and Meer, P. (2006, January 7\u201313). Region covariance: A fast descriptor for detection and classification. Proceedings of the European conference on computer vision, Graz, Austria.","DOI":"10.1007\/11744047_45"},{"key":"ref_65","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","article-title":"Face description with local binary patterns: Application to face recognition","volume":"28","author":"Ahonen","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","unstructured":"Gu, Q., Li, Z., and Han, J. (2021, September 20). Generalized Fisher Score for Feature Selection. Available online: https:\/\/arxiv.org\/abs\/1202.3725."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"9731","DOI":"10.1007\/s00521-019-04506-0","article-title":"Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks","volume":"32","author":"Wijnands","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"29059","DOI":"10.1007\/s11042-018-6378-6","article-title":"Driver drowsiness detection using hybrid convolutional neural network and long short-term memory","volume":"78","author":"Guo","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ed-Doughmi, Y., Idrissi, N., and Hbali, Y. (2020). Real-time system for driver fatigue detection based on a recurrent neuronal network. J. Imaging, 6.","DOI":"10.3390\/jimaging6030008"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6616584","article-title":"Driver fatigue detection based on convolutional neural networks using em-cnn","volume":"2020","author":"Zhao","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1016\/j.proeng.2012.06.298","article-title":"Analysis of Electroencephalography (EEG) signals and its categorization\u2014A study","volume":"38","author":"Kumar","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_74","first-page":"1","article-title":"What Is an Electrocardiogram (ECG)?","volume":"4","author":"Khorovets","year":"1999","journal-title":"Internet J. Adv. Nurs. Pract."},{"key":"ref_75","first-page":"195","article-title":"A review on wearable photoplethysmography sensors and their potential future applications in health care","volume":"4","author":"Castaneda","year":"2018","journal-title":"Int. J. Biosens. Bioelectron."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Mej\u00eda-Mej\u00eda, E., Budidha, K., Abay, T.Y., May, J.M., and Kyriacou, P.A. (2020). Heart rate variability (HRV) and pulse rate variability (PRV) for the assessment of autonomic responses. Front. Physiol., 11.","DOI":"10.3389\/fphys.2020.00779"},{"key":"ref_77","unstructured":"The McGill Physiology Lab (2021, September 20). Biological Signals Acquisition. Available online: https:\/\/www.medicine.mcgill.ca\/physio\/vlab\/Other_exps\/EOG\/eogintro_n.htm."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"12431","DOI":"10.3390\/s130912431","article-title":"Surface Electromyography Signal Processing and Classification Techniques","volume":"13","author":"Chowdhury","year":"2013","journal-title":"Sensors"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7169","DOI":"10.1109\/JSEN.2015.2473679","article-title":"Smartwatch-based wearable EEG system for driver drowsiness detection","volume":"15","author":"Li","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_80","first-page":"157","article-title":"Drowsiness detection based on EEG signal analysis using EMD and trained neural network","volume":"10","author":"Kaur","year":"2013","journal-title":"Int. J. Sci. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"7624","DOI":"10.1109\/JSEN.2019.2917850","article-title":"An effective hybrid model for EEG-based drowsiness detection","volume":"19","author":"Budak","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1046\/j.1440-1819.1999.00527.x","article-title":"Development of the polysomnographic database on CD-ROM","volume":"53","author":"Ichimaru","year":"1999","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"8855","DOI":"10.1109\/JSEN.2018.2869775","article-title":"Drowsiness detection using adaptive hermite decomposition and extreme learning machine for electroencephalogram signals","volume":"18","author":"Taran","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"7344","DOI":"10.1016\/j.eswa.2015.05.028","article-title":"Automatic detection of alertness\/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning","volume":"42","author":"Chen","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"146390","DOI":"10.1109\/ACCESS.2019.2946053","article-title":"Learning-based instantaneous drowsiness detection using wired and wireless electroencephalography","volume":"7","author":"Choi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/PROC.1982.12433","article-title":"Spectrum estimation and harmonic analysis","volume":"70","author":"Thomson","year":"1982","journal-title":"Proc. IEEE"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Chinara, S. (2021). Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. J. Neurosci. Methods, 347.","DOI":"10.1016\/j.jneumeth.2020.108927"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Percival, D.B., and Walden, A.T. (2000). Wavelet Methods for Time Series Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511841040"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/10.867928","article-title":"Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG","volume":"47","author":"Kemp","year":"2000","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_90","unstructured":"Kemp, B. (2021, September 20). Sleep-EDF Database Expanded. Available online: https:\/\/www.physionet.org\/content\/sleep-edfx\/1.0.0\/."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"026017","DOI":"10.1088\/1741-2552\/aa5a98","article-title":"A multimodal approach to estimating vigilance using EEG and forehead EOG","volume":"14","author":"Zheng","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/0167-2789(88)90081-4","article-title":"Approach to an irregular time series on the basis of the fractal theory","volume":"31","author":"Higuchi","year":"1988","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F. (1992). Individual comparisons by ranking methods. Breakthroughs in Statistics, Springer.","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1037\/h0059111","article-title":"A statistical consideration in psychological research","volume":"48","author":"Wilkinson","year":"1951","journal-title":"Psychol. Bull."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"6421","DOI":"10.1109\/JSEN.2020.3038440","article-title":"Entropy-Based Drowsiness Detection Using Adaptive Variational Mode Decomposition","volume":"21","author":"Khare","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Bandt, C., and Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Phys. Rev. Lett., 88.","DOI":"10.1103\/PhysRevLett.88.174102"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1515\/slgr-2015-0039","article-title":"Entropy-based algorithms in the analysis of biomedical signals","volume":"43","author":"Borowska","year":"2015","journal-title":"Stud. Log. Gramm. Rhetor."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1007\/s10439-009-9795-x","article-title":"Log energy entropy-based EEG classification with multilayer neural networks in seizure","volume":"37","author":"Kara","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Lee, H., Lee, J., and Shin, M. (2019). Using wearable ECG\/PPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics, 8.","DOI":"10.3390\/electronics8020192"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Koh, S., Cho, B.R., Lee, J.-i., Kwon, S.-O., Lee, S., Lim, J.B., Lee, S.B., and Kweon, H.-D. (2017, January 5\u20137). Driver drowsiness detection via PPG biosignals by using multimodal head support. Proceedings of the 2017 4th international conference on control, decision and information technologies (CoDIT), Barcelona, Spain.","DOI":"10.1109\/CoDIT.2017.8102622"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Kundinger, T., Sofra, N., and Riener, A. (2020). Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection. Sensors, 20.","DOI":"10.3390\/s20041029"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1109\/TBME.2018.2879346","article-title":"Heart rate variability-based driver drowsiness detection and its validation with EEG","volume":"66","author":"Fujiwara","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"81826","DOI":"10.1109\/ACCESS.2019.2924481","article-title":"Driver drowsiness detection based on respiratory signal analysis","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_105","unstructured":"Kamen, G., and Kinesiology, E. (2004). Research Methods in Biomechanics, Human Kinetics Publ."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Reaz, M., Hussain, M., and Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications (Correction). Biol. Proced. Online, 8.","DOI":"10.1251\/bpo124"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"8669","DOI":"10.1016\/j.eswa.2015.07.021","article-title":"Physiological signal based detection of driver hypovigilance using higher order spectra","volume":"42","author":"Sahayadhas","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1450006","DOI":"10.1142\/S0129065714500063","article-title":"Detection of driving fatigue by using noncontact EMG and ECG signals measurement system","volume":"24","author":"Fu","year":"2014","journal-title":"Int. J. Neural Syst."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Awais, M., Badruddin, N., and Drieberg, M. (2017). A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors, 17.","DOI":"10.3390\/s17091991"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TBME.2010.2077291","article-title":"Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm","volume":"58","author":"Khushaba","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_111","first-page":"1","article-title":"SRDA: An efficient algorithm for large-scale discriminant analysis","volume":"20","author":"Cai","year":"2007","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., and Han, J. (2007, January 28\u201331). Efficient kernel discriminant analysis via spectral regression. Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, USA.","DOI":"10.1109\/ICDM.2007.88"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"McDonald, A.D., Schwarz, C., Lee, J.D., and Brown, T.L. (2012, January 22\u201326). Real-time detection of drowsiness related lane departures using steering wheel angle. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Boston, MA, USA.","DOI":"10.1037\/e572172013-456"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Ma, J., Murphey, Y.L., and Zhao, H. (2015, January 7\u201310). Real time drowsiness detection based on lateral distance using wavelet transform and neural network. Proceedings of the 2015 IEEE symposium series on computational intelligence, Cape Town, South Africa.","DOI":"10.1109\/SSCI.2015.68"},{"key":"ref_115","unstructured":"Brown, T., Lee, J., Schwarz, C., Fiorentino, D., and McDonald, A. (2011). Final Report: Advanced Countermeasures for Multiple Impairments, National Highway Traffic Safety Administration."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Murphey, Y.L., Kochhar, D., Chen, F., Huang, Y., and Wang, Y. (2013). A Transportable Instrumentation Package for In-Vehicle On-Road Data Collection for Driver Research, SAE. Technical Paper: 0148-7191.","DOI":"10.4271\/2013-01-0202"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, S.E., Li, R., Cheng, B., and Shi, J. (2017). Online detection of driver fatigue using steering wheel angles for real driving conditions. Sensors, 17.","DOI":"10.3390\/s17030495"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Arefnezhad, S., Samiee, S., Eichberger, A., and Nahvi, A. (2019). Driver drowsiness detection based on steering wheel data applying adaptive neuro-fuzzy feature selection. Sensors, 19.","DOI":"10.3390\/s19040943"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.trd.2018.07.007","article-title":"Drowsiness monitoring based on steering wheel status","volume":"66","author":"Chai","year":"2019","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.aap.2015.09.002","article-title":"Driver drowsiness detection based on non-intrusive metrics considering individual specifics","volume":"95","author":"Wang","year":"2016","journal-title":"Accid. Anal. Prev."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"11829","DOI":"10.1109\/ACCESS.2019.2891971","article-title":"Driver drowsiness detection using multi-channel second order blind identifications","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.aap.2017.11.038","article-title":"Detection and prediction of driver drowsiness using artificial neural network models","volume":"126","author":"Bourdin","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_123","first-page":"1","article-title":"Utilization of a combined EEG\/NIRS system to predict driver drowsiness","volume":"7","author":"Nguyen","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.eswa.2018.07.054","article-title":"Automatic driver sleepiness detection using EEG, EOG and contextual information","volume":"115","author":"Barua","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/TITS.2018.2879609","article-title":"A smartphone-based drowsiness detection and warning system for automotive drivers","volume":"20","author":"Dasgupta","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_126","unstructured":"(2021, September 20). INVEDRIFAC\u2014A Video and Image Database of Faces of In-vehicle Automotive Drivers, India. Available online: https:\/\/sites.google.com\/site\/invedrifac\/."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Gwak, J., Hirao, A., and Shino, M. (2020). An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing. Appl. Sci., 10.","DOI":"10.3390\/app10082890"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Zilberg, E., Xu, Z.M., Burton, D., Karrar, M., and Lal, S. (2007, January 27\u201330). Methodology and initial analysis results for development of non-invasive and hybrid driver drowsiness detection systems. Proceedings of the the 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007), Sydney, Australia.","DOI":"10.1109\/AUSWIRELESS.2007.44"},{"key":"ref_129","unstructured":"Volvo (2021, September 20). Volvo Cars Introduces New Systems for Alerting Tired and Distracted Drivers. Available online: https:\/\/www.media.volvocars.com\/us\/en-us\/media\/pressreleases\/12130."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"162805","DOI":"10.1109\/ACCESS.2021.3131601","article-title":"Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning","volume":"9","author":"Altameem","year":"2021","journal-title":"IEEE Access"},{"key":"ref_131","first-page":"297","article-title":"Driver drowsiness measurement technologies: Current research, market solutions, and challenges","volume":"18","author":"Doudou","year":"2020","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"6701","DOI":"10.1109\/JSEN.2020.2975382","article-title":"Cloud-based driver monitoring system using a smartphone","volume":"20","author":"Kashevnik","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2021.3070419","article-title":"Estimation of Driver Vigilance Status Using Real-Time Facial Expression and Deep Learning","volume":"5","author":"Tamanani","year":"2021","journal-title":"IEEE Sens. Lett."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Abbas, Q., and Alsheddy, A. (2021). Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: A comparative analysis. Sensors, 21.","DOI":"10.3390\/s21010056"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TCE.2018.2872162","article-title":"Design and implementation of a drowsiness-fatigue-detection system based on wearable smart glasses to increase road safety","volume":"64","author":"Chang","year":"2018","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"124702","DOI":"10.1109\/ACCESS.2019.2937914","article-title":"A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system","volume":"7","author":"Dong","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:33:24Z","timestamp":1760135604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/2069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,7]]},"references-count":136,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22052069"],"URL":"https:\/\/doi.org\/10.3390\/s22052069","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,7]]}}}