{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:37:34Z","timestamp":1770910654534,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["62172066"],"award-info":[{"award-number":["62172066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["U21A20448"],"award-info":[{"award-number":["U21A20448"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s12652-022-04325-7","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T11:03:56Z","timestamp":1658747036000},"page":"12493-12509","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An fNIRS labeling image feature-based customized driving fatigue detection method"],"prefix":"10.1007","volume":"14","author":[{"given":"Lingqiu","family":"Zeng","sequence":"first","affiliation":[]},{"given":"Kun","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1517-7701","authenticated-orcid":false,"given":"Qingwen","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"issue":"1","key":"4325_CR1","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.bbe.2020.08.009","volume":"41","author":"A Ahmadi","year":"2021","unstructured":"Ahmadi A, Bazregarzadeh H, Kazemi K (2021) Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity. Biocybern Biomed Eng 41(1):316\u2013332","journal-title":"Biocybern Biomed Eng"},{"key":"4325_CR2","first-page":"219","volume":"10","author":"S Ahn","year":"2016","unstructured":"Ahn S, Nguyen T, Jang H, Kim JG, Jun SC (2016) Exploring neuro-physiological correlates of drivers\u2019 mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and FNIRS data. Front Hum Neurosci 10:219","journal-title":"Front Hum Neurosci"},{"issue":"1\u20132","key":"4325_CR3","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3109\/00207459008994241","volume":"52","author":"T Akerstedt","year":"1990","unstructured":"Akerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. Int J Neurosci 52(1\u20132):29\u201337","journal-title":"Int J Neurosci"},{"key":"4325_CR4","first-page":"25","volume":"20","author":"B Akrout","year":"2021","unstructured":"Akrout B, Mahdi W (2021) A novel approach for driver fatigue detection based on visual characteristics analysis. J Ambient Intell Human Comput 20:25","journal-title":"J Ambient Intell Human Comput"},{"key":"4325_CR5","first-page":"14","volume":"20","author":"N Alioua","year":"2014","unstructured":"Alioua N, Amine A, Rziza M (2014) Driver\u2019s fatigue detection based on yawning extraction. Int J Veh Technol 20:14","journal-title":"Int J Veh Technol"},{"key":"4325_CR6","doi-asserted-by":"crossref","unstructured":"Ayachi R, Afif M, Said Y, Abdelali AB (2021) Drivers fatigue detection using efficientdet in advanced driver assistance systems. In: 2021 18th international multi-conference on systems, signals and devices (SSD), pp 738\u2013742. IEEE","DOI":"10.1109\/SSD52085.2021.9429294"},{"key":"4325_CR7","doi-asserted-by":"crossref","unstructured":"Azarnoosh M, Mohammadi MR, Nasrabadi AM, Firoozabadi SMP (2010) Evaluating variability of frequency features of EEG signals during mental fatigue. In: 2010 17th Iranian conference of biomedical engineering (ICBME), pp 1\u20134. IEEE","DOI":"10.1109\/ICBME.2010.5704977"},{"issue":"5","key":"4325_CR8","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1109\/TITS.2021.3090272","volume":"23","author":"B Bakker","year":"2022","unstructured":"Bakker B, Zab\u0142ocki B, Baker A, Riethmeister V, Marx B, Iyer G, Anund A, Ahlstr\u00f6m C (2022) A multi-stage, multi-feature machine learning approach to detect driver sleepiness in naturalistic road driving conditions. IEEE Trans Intell Transp Syst 23(5):4791\u20134800","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"4325_CR9","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1109\/TIP.2021.3053369","volume":"30","author":"R Bhatt","year":"2021","unstructured":"Bhatt R, Naik N, Subramanian VK (2021) Ssim compliant modeling framework with denoising and deblurring applications. IEEE Trans Image Process 30:2611\u20132626","journal-title":"IEEE Trans Image Process"},{"key":"4325_CR10","doi-asserted-by":"crossref","unstructured":"Chen M, Li F, Lei J, Zeng Z, Han Q, Chen Q (2017) Driving fatigue detecting method based on temperature insensitive ECG parameters. In: International conference on internet of vehicles. Springer, pp 105\u2013118","DOI":"10.1007\/978-3-319-72329-7_10"},{"issue":"3","key":"4325_CR11","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1109\/JBHI.2020.3008229","volume":"25","author":"W Dang","year":"2020","unstructured":"Dang W, Gao Z, Lv D, Sun X, Cheng C (2020) Rhythm-dependent multilayer brain network for the detection of driving fatigue. IEEE J Biomed Health Inform 25(3):693\u2013700","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"4325_CR12","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.3390\/s140101106","volume":"14","author":"IG Daza","year":"2014","unstructured":"Daza IG, Bergasa LM, Bronte S, Yebes JJ, Almaz\u00e1n J, Arroyo R (2014) Fusion of optimized indicators from advanced driver assistance systems (ADAS) for driver drowsiness detection. Sensors (Basel, Switzerland) 14(1):1106\u20131131","journal-title":"Sensors (Basel, Switzerland)"},{"key":"4325_CR13","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"issue":"1","key":"4325_CR14","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/sleep\/4.1.83","volume":"4","author":"J Herscovitch","year":"1981","unstructured":"Herscovitch J, Broughton R (1981) Sensitivity of the Stanford sleepiness scale to the effects of cumulative partial sleep deprivation and recovery oversleeping. Sleep 4(1):83\u201392","journal-title":"Sleep"},{"key":"4325_CR15","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/j.1469-8986.1973.tb00801.x","volume":"10","author":"E Hoddes","year":"1972","unstructured":"Hoddes E, Dement W, Zarcone V (1972) The development and use of the Stanford sleepiness scale (SSS). Psychophysiology 10:431\u2013436","journal-title":"Psychophysiology"},{"key":"4325_CR16","doi-asserted-by":"crossref","unstructured":"Huynh X-P, Park S-M, Kim Y-G (2016) Detection of driver drowsiness using 3d deep neural network and semi-supervised gradient boosting machine. In: Asian conference on computer vision. Springer, pp 134\u2013145","DOI":"10.1007\/978-3-319-54526-4_10"},{"key":"4325_CR17","doi-asserted-by":"crossref","first-page":"147054","DOI":"10.1109\/ACCESS.2021.3123388","volume":"9","author":"H Jia","year":"2021","unstructured":"Jia H, Xiao Z, Ji P (2021) Fatigue driving detection based on deep learning and multi-index fusion. IEEE Access 9:147054\u2013147062","journal-title":"IEEE Access"},{"issue":"7","key":"4325_CR18","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1016\/j.clinph.2006.03.011","volume":"117","author":"K Kaida","year":"2006","unstructured":"Kaida K, Takahashi M, \u00c5kerstedt T, Nakata A, Otsuka Y, Haratani T, Fukasawa K (2006) Validation of the karolinska sleepiness scale against performance and EEG variables. Clin Neurophysiol 117(7):1574\u20131581","journal-title":"Clin Neurophysiol"},{"key":"4325_CR19","doi-asserted-by":"crossref","unstructured":"Klaiber M, Sauter D, Baumgartl H, Buettner R (2021) A systematic literature review on transfer learning for 3d-cnns. In: 2021 international joint conference on neural networks (IJCNN), pp 1\u201310. IEEE","DOI":"10.1109\/IJCNN52387.2021.9533302"},{"issue":"1","key":"4325_CR20","first-page":"37","volume":"4","author":"H Ku","year":"2020","unstructured":"Ku H, Dong W (2020) Face recognition based on mtcnn and convolutional neural network. Front Signal Process 4(1):37\u201342","journal-title":"Front Signal Process"},{"key":"4325_CR21","doi-asserted-by":"crossref","unstructured":"Lam C, Epps J, Chen S (2021) Wearable fatigue detection based on blink-saccade synchronisation. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC), pp 1186\u20131191. IEEE","DOI":"10.1109\/SMC52423.2021.9659006"},{"key":"4325_CR22","doi-asserted-by":"crossref","first-page":"5723","DOI":"10.1109\/ACCESS.2017.2686424","volume":"5","author":"J Lei","year":"2017","unstructured":"Lei J, Han Q, Chen L, Lai Z, Zeng L, Liu X (2017) A novel side face contour extraction algorithm for driving fatigue statue recognition. IEEE Access 5:5723\u20135730","journal-title":"IEEE Access"},{"key":"4325_CR23","doi-asserted-by":"crossref","unstructured":"Lei J, Liu F, Han Q, Tang Y, Zeng L, Chen M, Ye L, Jin L (2018) Study on driving fatigue evaluation system based on short time period ECG signal. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 2466\u20132470. IEEE","DOI":"10.1109\/ITSC.2018.8569409"},{"issue":"3","key":"4325_CR24","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s00421-009-1122-6","volume":"107","author":"Z Li","year":"2009","unstructured":"Li Z, Zhang M, Zhang X, Dai S, Yu X, Wang Y (2009) Assessment of cerebral oxygenation during prolonged simulated driving using near infrared spectroscopy: its implications for fatigue development. Eur J Appl Physiol 107(3):281\u2013287","journal-title":"Eur J Appl Physiol"},{"key":"4325_CR25","volume":"82","author":"R Li","year":"2021","unstructured":"Li R, Chen YV, Zhang L (2021) A method for fatigue detection based on driver\u2019s steering wheel grip. Int J Ind Ergon 82:103083","journal-title":"Int J Ind Ergon"},{"issue":"10\u201311","key":"4325_CR26","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.1177\/0954407021999485","volume":"235","author":"X Li","year":"2021","unstructured":"Li X, Xia J, Cao L, Zhang G, Feng X (2021) Driver fatigue detection based on convolutional neural network and face alignment for edge computing device. Proc Inst Mech Eng Part D J Autom Eng 235(10\u201311):2699\u20132711","journal-title":"Proc Inst Mech Eng Part D J Autom Eng"},{"key":"4325_CR27","first-page":"9","volume":"30","author":"CT Lin","year":"2019","unstructured":"Lin CT, King JT, Chuang CH, Ding W, Wang YK (2019) Exploring the brain responses to driving fatigue through simultaneous EEG and FNIRS measurements. Int J Neural Syst 30:9","journal-title":"Int J Neural Syst"},{"key":"4325_CR28","first-page":"1","volume":"99","author":"Y Liu","year":"2019","unstructured":"Liu Y, Zhang T, Li Z (2019) Dcnn-based real-time driver fatigue behavior detection in urban rail transit. IEEE Access 99:1","journal-title":"IEEE Access"},{"key":"4325_CR29","volume":"71","author":"Z Liu","year":"2019","unstructured":"Liu Z, Peng Y, Hu W (2019) Driver fatigue detection based on deeply-learned facial expression representation. J Vis Commun Image Represent 71:102723","journal-title":"J Vis Commun Image Represent"},{"key":"4325_CR30","doi-asserted-by":"crossref","unstructured":"Lu Y, Wang Z (2007) Detecting driver yawning in successive images. In: 2007 1st international conference on bioinformatics and biomedical engineering, pp 581\u2013583. IEEE","DOI":"10.1109\/ICBBE.2007.152"},{"issue":"02","key":"4325_CR31","doi-asserted-by":"crossref","first-page":"2252007","DOI":"10.1142\/S0218001422520073","volume":"36","author":"H Mao","year":"2022","unstructured":"Mao H, Tang J, Zhao X, Tang M, Jiang Z (2022) A driver drowsiness detection scheme based on 3d convolutional neural networks. Int J Pattern Recognit Artif Intell 36(02):2252007","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"4325_CR32","volume":"69","author":"J Min","year":"2021","unstructured":"Min J, Xiong C, Zhang Y, Cai M (2021) Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model. Biomed Signal Process Control 69:102857","journal-title":"Biomed Signal Process Control"},{"key":"4325_CR33","unstructured":"NHTSA (2020) Preview of motor vehicle crashes in 2019. NHTSA\u2019s National Center for Statistics and Analysis, 1"},{"issue":"6","key":"4325_CR34","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s11604-019-00826-2","volume":"37","author":"T Nihashi","year":"2019","unstructured":"Nihashi T, Ishigaki T, Satake H, Ito S, Kaii O, Mori Y, Shimamoto K, Fukushima H, Suzuki K, Umakoshi H et al (2019) Monitoring of fatigue in radiologists during prolonged image interpretation using fnirs. Jpn J Radiol 37(6):437\u2013448","journal-title":"Jpn J Radiol"},{"issue":"3","key":"4325_CR35","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3390\/aerospace9030173","volume":"9","author":"T Pan","year":"2022","unstructured":"Pan T, Wang H, Si H, Liu H, Xu M (2022) Research on the identification of pilots\u2019 fatigue status based on functional near-infrared spectroscopy. Aerospace 9(3):173","journal-title":"Aerospace"},{"issue":"2","key":"4325_CR36","first-page":"104","volume":"7","author":"Z Pei","year":"2002","unstructured":"Pei Z, Zhenghe S, Yiming Z (2002) Perclos-based recognition algorithms of motor driver fatigue. J China Agric Univ 7(2):104\u2013109","journal-title":"J China Agric Univ"},{"key":"4325_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpsychores.2020.110229","volume":"137","author":"A Penson","year":"2020","unstructured":"Penson A, van Deuren S, Worm-Smeitink M, Bronkhorst E, van den Hoogen FHJ, van Engelen BGM, Peters M, Bleijenberg G, Vercoulen JH, Blijlevens N, van Dulmen-den Broeder E, Loonen J, Knoop H (2020) Short fatigue questionnaire: screening for severe fatigue. J Psychosom Res 137:110229","journal-title":"J Psychosom Res"},{"issue":"17","key":"4325_CR38","doi-asserted-by":"crossref","first-page":"10787","DOI":"10.1007\/s00521-020-05046-8","volume":"33","author":"D Po\u0142ap","year":"2021","unstructured":"Po\u0142ap D, Srivastava G (2021) Neural image reconstruction using a heuristic validation mechanism. Neural Comput Appl 33(17):10787\u201310797","journal-title":"Neural Comput Appl"},{"key":"4325_CR39","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107872","volume":"113","author":"D Po\u0142ap","year":"2021","unstructured":"Po\u0142ap D, Wo\u017aniak M (2021) Meta-heuristic as manager in federated learning approaches for image processing purposes. Appl Soft Comput 113:107872","journal-title":"Appl Soft Comput"},{"key":"4325_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3147367","volume":"60","author":"D Po\u0142ap","year":"2022","unstructured":"Po\u0142ap D, Wawrzyniak N, W\u0142odarczyk-Sielicka M (2022) Side-scan sonar analysis using roi analysis and deep neural networks. IEEE Trans Geosci Remote Sens 60:1\u20138","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"4325_CR41","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1016\/j.clinph.2013.01.018","volume":"124","author":"AA Putilov","year":"2013","unstructured":"Putilov AA, Donskaya OG (2013) Construction and validation of the EEG analogues of the karolinska sleepiness scale based on the karolinska drowsiness test. Clin Neurophysiol 124(7):1346\u20131352","journal-title":"Clin Neurophysiol"},{"key":"4325_CR42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neuroimage.2013.06.016","volume":"85","author":"S Tak","year":"2014","unstructured":"Tak S, Ye JC (2014) Statistical analysis of fnirs data: a comprehensive review. Neuroimage 85:72\u201391","journal-title":"Neuroimage"},{"key":"4325_CR43","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"4325_CR44","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11571-020-09601-w","volume":"15","author":"T Tuncer","year":"2021","unstructured":"Tuncer T, Dogan S, Ertam F, Subasi A (2021) A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cogn Neurodyn 15:7","journal-title":"Cogn Neurodyn"},{"issue":"4","key":"4325_CR45","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"4325_CR46","doi-asserted-by":"crossref","first-page":"196","DOI":"10.3390\/e20030196","volume":"20","author":"F Wang","year":"2018","unstructured":"Wang F, Hong W, Fu R (2018) Real-time ECG-based detection of fatigue driving using sample entropy. Entropy 20(3):196","journal-title":"Entropy"},{"issue":"4","key":"4325_CR47","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s11571-018-9481-5","volume":"12","author":"H Wang","year":"2018","unstructured":"Wang H, Dragomir A, Abbasi NI, Li J, Thakor NV, Bezerianos A (2018) A novel real-time driving fatigue detection system based on wireless dry EEG. Cogn Neurodyn 12(4):365\u2013376","journal-title":"Cogn Neurodyn"},{"issue":"1","key":"4325_CR48","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/TSMC.2021.3062715","volume":"52","author":"EQ Wu","year":"2022","unstructured":"Wu EQ, Xiong P, Tang ZR, Li GJ, Song A, Zhu LM (2022) Detecting dynamic behavior of brain fatigue through 3-d-cnn-lstm. IEEE Trans Syst Man Cybern Syst 52(1):90\u2013100","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"4325_CR49","doi-asserted-by":"crossref","unstructured":"Xu S, Zhao X-h, Zhang X-J, Rong J (2011) A study of the identification method of driving fatigue based on physiological signals. In: ICCTP 2011: towards sustainable transportation systems, pp 2296\u20132307","DOI":"10.1061\/41186(421)229"},{"issue":"3","key":"4325_CR50","first-page":"1563","volume":"63","author":"P Yan","year":"2020","unstructured":"Yan P, Sun Y, Li Z, Zou J, Hong D (2020) Driver fatigue detection system based on colored and infrared eye features fusion. Comput Mater Contin 63(3):1563\u20131574","journal-title":"Comput Mater Contin"},{"issue":"4","key":"4325_CR51","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TSMCA.2009.2018634","volume":"39","author":"JH Yang","year":"2009","unstructured":"Yang JH, Mao Z-H, Tijerina L, Pilutti T, Coughlin J, Feron E (2009) Detection of driver fatigue caused by sleep deprivation. IEEE Trans Syst Man Cybern Part A Syst Humans 39(4):694\u2013705","journal-title":"IEEE Trans Syst Man Cybern Part A Syst Humans"},{"issue":"19","key":"4325_CR52","doi-asserted-by":"crossref","first-page":"9195","DOI":"10.3390\/app11199195","volume":"11","author":"M Ye","year":"2021","unstructured":"Ye M, Zhang W, Cao P, Liu K (2021) Driver fatigue detection based on residual channel attention network and head pose estimation. Appl Sci 11(19):9195","journal-title":"Appl Sci"},{"issue":"3","key":"4325_CR53","doi-asserted-by":"crossref","first-page":"916","DOI":"10.3390\/s22030916","volume":"22","author":"Z Yin","year":"2022","unstructured":"Yin Z, Liu B, Hao D, Yang L, Feng Y (2022) Evaluation of vdt-induced visual fatigue by automatic detection of blink features. Sensors 22(3):916","journal-title":"Sensors"},{"issue":"10","key":"4325_CR54","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"4325_CR55","doi-asserted-by":"crossref","unstructured":"Zhang F, Su J, Geng L, Xiao Z (2017) Driver fatigue detection based on eye state recognition. In: 2017 international conference on machine vision and information technology (CMVIT), pp 105\u2013110. IEEE","DOI":"10.1109\/CMVIT.2017.25"},{"key":"4325_CR56","first-page":"7251280","volume":"2020","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Zhou N, Zhang L, Yan H, Xu Y, Zhang Z (2020) Driver fatigue detection based on convolutional neural networks using EM-CNN. Comput Intell Neurosci 2020:7251280","journal-title":"Comput Intell Neurosci"},{"issue":"4","key":"4325_CR57","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1049\/ipr2.12207","volume":"16","author":"G Zhao","year":"2022","unstructured":"Zhao G, He Y, Yang H, Tao Y (2022) Research on fatigue detection based on visual features. IET Image Proc 16(4):1044\u20131053","journal-title":"IET Image Proc"},{"issue":"4","key":"4325_CR58","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.3390\/app12042224","volume":"12","author":"T Zhu","year":"2022","unstructured":"Zhu T, Zhang C, Wu T, Ouyang Z, Li H, Na X, Liang J, Li W (2022) Research on a real-time driver fatigue detection algorithm based on facial video sequences. Appl Sci 12(4):2224","journal-title":"Appl Sci"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04325-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-04325-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-04325-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T16:33:20Z","timestamp":1690216400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-04325-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":58,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4325"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-04325-7","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]},"assertion":[{"value":"15 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}