Abbas Q (2020) Hybridfatigue: a real-time driver drowsiness detection using hybrid features and transfer learning. Int J Adv Comput Sci Appl 11:585–593
Abbas Q, Alsheddy A (2021) Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis. Sensors 21:56
Alamoodi A et al (2021) Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Comput Biol Med 139:104957
Article CAS PubMed PubMed Central Google Scholar
Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28:R1
Ashraf I, Hur S, Shafiq M, Park Y (2019) Catastrophic factors involved in road accidents: underlying causes and descriptive analysis. PLoS ONE 14:e0223473
Article CAS PubMed PubMed Central Google Scholar
Attarodi G, Nikooei SM, Dabanloo NJ, Pourmasoumi P, Tareh A (2018) Detection of driver’s drowsiness using new features extracted from HRV signal. In: 2018 computing in cardiology conference (CinC), vol 45, pp 1–4. IEEE
Awais M, Badruddin N, Drieberg M (2017) A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17:1991
Article PubMed Central Google Scholar
Awais M, Badruddin N, Drieberg M (2014) Driver drowsiness detection using eeg power spectrum analysis. In: 2014 IEEE Region 10 symposium, pp 244–247. IEEE
Babaeian M, Mozumdar M (2019) Driver drowsiness detection algorithms using electrocardiogram data analysis. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 0001–0006 (IEEE)
Babusiak B, Hajducik A, Medvecky S, Lukac M, Klarak J (2021) Design of smart steering wheel for unobtrusive health and drowsiness monitoring. Sensors 21:5285
Article PubMed PubMed Central Google Scholar
Bakker J, Pechenizkiy M, Sidorova N (2011) What’s your current stress level? detection of stress patterns from GSR sensor data. In: 2011 IEEE 11th international conference on data mining workshops, pp 573–580. IEEE
Balam VP, Chinara S (2021) Statistical channel selection method for detecting drowsiness through single-channel EEG-based BCI system. IEEE Trans Instrum Meas 70:1–9
Balam VP, Sameer VU, Chinara S (2021) Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram. IET Intell Transp Syst 15:514–524
Bartolacci C et al (2020) The influence of sleep quality, vigilance, and sleepiness on driving-related cognitive abilities: a comparison between young and older adults. Brain Sci 10:327
Article PubMed Central Google Scholar
Barua S, Ahmed MU, Begum S (2020) Towards intelligent data analytics: a case study in driver cognitive load classification. Brain Sci 10:526
Article PubMed Central Google Scholar
Bhardwaj R, Natrajan P, Balasubramanian V (2018) Study to determine the effectiveness of deep learning classifiers for ECG based driver fatigue classification. In: 2018 IEEE 13th international conference on industrial and information systems (ICIIS), pp 98–102. IEEE
Cai Y, Goldberg AN, Chang JL (2020) The nose and nasal breathing in sleep apnea. Otolaryngol Clin N Am 53:385–395
Cardone D et al. (2021) Driver drowsiness evaluation by means of thermal infrared imaging: preliminary results. In: infrared sensors, devices, and applications XI, vol 11831, 118310P, International Society for Optics and Photonics
Caryn FH, Rahadianti L (2021) Driver drowsiness detection based on drivers’ physical behaviours: a systematic literature review. Comput Eng Appl J 10:161–175
Chang T-C, Wu M-H, Kim P-Z, Yu M-H (2021) Smart driver drowsiness detection model based on analytic hierarchy process. Sens Mater 33:485–497
Chen L-L, Zhang A, Lou X-G (2019) Cross-subject driver status detection from physiological signals based on hybrid feature selection and transfer learning. Expert Syst Appl 137:266–280
Chen J, Wang S, He E, Wang H, Wang L (2021) Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach. Biomed Signal Proc Control 69:102792
Chinara S et al (2021) Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal. J Neurosci Methods 347:108927
Choi M, Koo G, Seo M, Kim SW (2017) Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Trans Instrum Meas 67:634–645
Clariavate Web of Science (2021) Web of science: summary of coverage. https://clarivate.libguides.com/webofscienceplatform/coverage
Dani VS, Freitas CMDS, Ten Thom LH (2019) Ten years of visualization of business process models: a systematic literature review. Comput Stand Interfaces 66:103347
Darzi A, Gaweesh SM, Ahmed MM, Novak D (2018) Identifying the causes of drivers’ hazardous states using driver characteristics, vehicle kinematics, and physiological measurements. Front Neurosci 12:568
Article PubMed PubMed Central Google Scholar
Doudou M, Bouabdallah A, Berge-Cherfaoui V (2020) Driver drowsiness measurement technologies: current research, market solutions, and challenges. Int J Intell Transp Syst Res 18:297–319
Du G, Li T, Li C, Liu PX, Li D (2020) Vision-based fatigue driving recognition method integrating heart rate and facial features. IEEE Trans Intell Transp Syst 22:3089–3100
Dunbar J, Gilbert JE, Lewis B (2020) Exploring differences between self-report and electrophysiological indices of drowsy driving: a usability examination of a personal brain-computer interface device. J Saf Res 74:27–34
Forczmański P, Kutelski K (2018) Driver drowsiness estimation by means of face depth map analysis. In: International multi-conference on advanced computer systems, pp 396–407, Springer, Berlin
Forczmański P, Smoliński A (2021) Supporting driver physical state estimation by means of thermal image processing. In: International conference on computational science, pp 149–163, Springer, Berlin
Gielen J, Aerts J-M (2019) Feature extraction and evaluation for driver drowsiness detection based on thermoregulation. Appl Sci 9:3555
Gjoreski M et al (2020) Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals. IEEE Access 8:70590–70603
Gromer M, Salb D, Walzer T, Madrid NM, Seepold R (2019) ECG sensor for detection of driver’s drowsiness. Procedia Comput Sci 159:1938–1946
Gu X et al (2018) Non-contact fatigue driving detection using CW doppler radar. In: 2018 IEEE MTT-S international wireless symposium (IWS), pp 1–3. IEEE
Guede-Fernandez F, Fernandez-Chimeno M, Ramos-Castro J, Garcia-Gonzalez MA (2019) Driver drowsiness detection based on respiratory signal analysis. IEEE Access 7:81826–81838
Gupta N, Najeeb D, Gabrielian V, Nahapetian A (2017) Mobile ecg-based drowsiness detection. In: 2017 14th IEEE annual consumer communications & networking conference (CCNC), pp 29–32. IEEE
Gwak J, Hirao A, Shino M (2020) An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing. Appl Sci 10:2890
Gwak J, Shino M, Hirao A (2018) Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 1794–1800. IEEE
Haghani M et al (2021) Applications of brain imaging methods in driving behaviour research. Accid Anal Prev 154:106093
Helakari H et al (2020) Sleep-specific changes in physiological brain pulsations. bioRxiv
Hendra M, Kurniawan D, Chrismiantari RV, Utomo TP, Nuryani N (2019) Drowsiness detection using heart rate variability analysis based on microcontroller unit. J Phys Conf Ser 1153:012047
Houshmand S, Kazemi R, Salmanzadeh H (2021) A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles. Proc Inst Mech Eng Part H J Eng Med 235:1069–1078
Hu J (2017) Comparison of different features and classifiers for driver fatigue detection based on a single eeg channel. Comput Math Methods Med 2017:5109530. https://doi.org/10.1155/2017/5109530
Article PubMed PubMed Central Google Scholar
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
Josephin JF, Lakshmi C, James SJ (2020) A review on the measures and techniques adapted for the detection of driver drowsiness. In: IOP conference series: materials science and engineering, vol 993, p 012101 (IOP Publishing)
Kajiwara S (2021) Driver-condition detection using a thermal imaging camera and neural networks. Int J Automot Technol 22:1505–1515
Karuppusamy NS, Kang B-Y (2020) Multimodal system to detect driver fatigue using EEG, gyroscope, and image processing. IEEE Access 8:129645–129667
Kendall S (2019) Pubmed, web of science, or Google Scholar. A behind-the-scenes guide for life scientists. Which one is best: PubMed, Web of Science, or Google Scholar
Khalfallah K et al (2010) Noninvasive galvanic skin sensor for early diagnosis of sudomotor dysfunction: application to diabetes. IEEE Sens J 12:456–463
Khushaba RN, Kodagoda S, Lal S, Dissanayake G (2010) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans Biomed Eng 58:121–131
Kiashari SEH, Nahvi A, Homayounfard A, Bakhoda H (2018) Monitoring the variation in driver respiration rate from wakefulness to drowsiness: a non-intrusive method for drowsiness detection using thermal imaging. J Sleep Sci 3:1–9
Kiashari SEH, Nahvi A, Bakhoda H, Homayounfard A, Tashakori M (2020) Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator. Multimed Tools Appl 79:17793
Kim J, Shin M (2019) Utilizing HRV-derived respiration measures for driver drowsiness detection. Electronics 8:669
Knapik M, Cyganek B (2019) Driver’s fatigue recognition based on yawn detection in thermal images. Neurocomputing 338:274–292
Kondapaneni A, Hemanth C, Sangeetha R, Vaishnavi Priyanka R, Sanjay Saradhi M (2021) A smart drowsiness detection system for accident prevention. Natl Acad Sci Lett 44:317–320
Krishnan P, Yaacob S, Krishnan AP, Rizon M, Ang CK (2020) EEG based drowsiness detection using relative band power and short-time Fourier transform. J Robot Netw Artif Life 7:147–151
Kundinger T, Sofra N, Riener A (2020) Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection. Sensors 20:1029
Article PubMed Central Google Scholar
LaRocco J, Le MD, Paeng D-G (2020) A systemic review of available low-cost EEG headsets used for drowsiness detection. Front Neuroinformatics 14:42
Lavanya K, Bajaj S, Tank P, Jain S (2017) Handwritten digit recognition using Hoeffding tree, decision tree and random forests-a comparative approach. In: 2017 international conference on computational intelligence in data science (ICCIDS), pp 1–6. IEEE
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