{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T16:48:07Z","timestamp":1748623687093,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031600111"},{"type":"electronic","value":"9783031600128"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-60012-8_11","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:02:20Z","timestamp":1717203740000},"page":"172-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Non-visual Effects Driven Fatigue Level Recognition Method for Enclosed Space Workers"],"prefix":"10.1007","author":[{"given":"Xian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingluan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengkai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"issue":"1","key":"11_CR1","first-page":"12","volume":"1","author":"D Chen","year":"2023","unstructured":"Chen, D., Zhu, M., Qiao, Y., Wang, J., Zhang, X.: An ergonomic design method of manned cabin driven by human operation performance. Adv. Des. Res. 1(1), 12\u201320 (2023)","journal-title":"Adv. Des. Res."},{"key":"11_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2023.110616","volume":"243","author":"Z Gou","year":"2023","unstructured":"Gou, Z., Gou, B., Liao, W., Bao, Y., Deng, Y.: Integrated lighting ergonomics: a review on the association between non-visual effects of light and ergonomics in the enclosed cabins. Build. Environ. 243, 110616 (2023)","journal-title":"Build. Environ."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Berson, D.M., Dunn, F.A., Takao, M.J.: Phototransduction by retinal ganglion cells that set the circadian clock. Science 295(5557), 1070\u20131073 (2002)","DOI":"10.1126\/science.1067262"},{"issue":"2","key":"11_CR4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1177\/1477153520958448","volume":"53","author":"KW Houser","year":"2020","unstructured":"Houser, K.W., Boyce, P.R., Zeitzer, J.M., Herf, M.: Human-centric lighting: Myth, magic or metaphor? Light. Res. Technol. 53(2), 97\u2013118 (2020)","journal-title":"Light. Res. Technol."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Mills, P.R., Tomkins, S.C., Schlangen, L.J.M.: The effect of high correlated colour temperature office lighting on employee wellbeing and work performance 5 (2007)","DOI":"10.1186\/1740-3391-5-2"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Li, Y., Ru, T., Chen, Q., Qian, L., Luo, X., Zhou, G.: Effects of illuminance and correlated color temperature of indoor light on emotion perception 11, 14351 (2021)","DOI":"10.1038\/s41598-021-93523-y"},{"key":"11_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.161933","volume":"870","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Qiao, Y., Wang, H., Wang, J., Chen, D.: Lighting environmental assessment in enclosed spaces based on emotional model. Sci. Total. Environ. 870, 161933 (2023)","journal-title":"Sci. Total. Environ."},{"key":"11_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.106930","volume":"199","author":"B Xu","year":"2020","unstructured":"Xu, B., Wu, Q., Xi, C., He, R.: Recognition of the fatigue status of pilots using BF\u2013PSO optimized multi-class GP classification with sEMG signals. Reliab. Eng. Syst. Saf. 199, 106930 (2020)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"10","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1016\/j.ins.2010.01.011","volume":"180","author":"G Yang","year":"2010","unstructured":"Yang, G., Lin, Y., Bhattacharya, P.: A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf. Sci. 180(10), 1942\u20131954 (2010)","journal-title":"Inf. Sci."},{"key":"11_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2021.103083","volume":"82","author":"R Li","year":"2021","unstructured":"Li, R., Chen, Y.V., Zhang, L.: A method for fatigue detection based on Driver\u2019s steering wheel grip. Int. J. Ind. Ergon. 82, 103083 (2021)","journal-title":"Int. J. Ind. Ergon."},{"key":"11_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104413","volume":"133","author":"S Chen","year":"2021","unstructured":"Chen, S., et al.: Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas. Comput. Biol. Med. 133, 104413 (2021)","journal-title":"Comput. Biol. Med."},{"key":"11_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2023.106103","volume":"163","author":"A Ibrahim","year":"2023","unstructured":"Ibrahim, A., Nnaji, C., Namian, M., Koh, A., Techera, U.: Investigating the impact of physical fatigue on construction workers\u2019 situational awareness. Saf. Sci. 163, 106103 (2023)","journal-title":"Saf. Sci."},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.nucengdes.2023.112534","volume":"414","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Sun, L., Zhang, H., Yuan, X., Cui, K.: Effects of video display terminal fatigue on situational awareness ability of operators and modeling study. Nucl. Eng. Des. 414, 112534 (2023)","journal-title":"Nucl. Eng. Des."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload. Fatigue Drowsiness 44(Sp. Iss. SI), 58\u201375 (2014)","DOI":"10.1016\/j.neubiorev.2012.10.003"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Sun, Y., Lim, J., Meng, J., Kwok, K., Thakor, N., Bezerianos, A.: Discriminative analysis of brain functional connectivity patterns for mental fatigue classification. Engineering 42(10), 2084\u20132094 (2014)","DOI":"10.1007\/s10439-014-1059-8"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Aryal, A., Ghahramani, A., Becerik-Gerber, B.: Monitoring fatigue in construction workers using physiological measurements. 82(oct.), 154\u2013165 (2017)","DOI":"10.1016\/j.autcon.2017.03.003"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1016\/j.procs.2018.04.094","volume":"130","author":"M Ulinskas","year":"2018","unstructured":"Ulinskas, M., Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., Wo\u017aniak, M.: Recognition of human daytime fatigue using keystroke data. Procedia Comput. Sci. 130, 947\u2013952 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110613","volume":"146","author":"P Li","year":"2023","unstructured":"Li, P., et al.: An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue. Appl. Soft Comput. 146, 110613 (2023)","journal-title":"Appl. Soft Comput."},{"key":"11_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106981","volume":"126","author":"Z Sun","year":"2023","unstructured":"Sun, Z., Miao, Y., Jeon, J.Y., Kong, Y., Park, G.: Facial feature fusion convolutional neural network for driver fatigue detection. Eng. Appl. Artif. Intell. 126, 106981 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"11_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103198","volume":"71","author":"C Wei","year":"2022","unstructured":"Wei, C., et al.: Recognition of lower limb movements using empirical mode decomposition and k-nearest neighbor entropy estimator with surface electromyogram signals. Biomed. Signal Process. Control 71, 103198 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.neucom.2017.12.062","volume":"283","author":"Z Yin","year":"2018","unstructured":"Yin, Z., Zhang, J.: Task-generic mental fatigue recognition based on neurophysiological signals and dynamical deep extreme learning machine. Neurocomputing 283, 266\u2013281 (2018)","journal-title":"Neurocomputing"},{"issue":"4","key":"11_CR22","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.irbm.2022.06.001","volume":"43","author":"C Tepe","year":"2022","unstructured":"Tepe, C., Demir, M.C.: Real-time classification of EMG Myo armband data using support vector machine. IRBM 43(4), 300\u2013308 (2022)","journal-title":"IRBM"},{"issue":"7","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1016\/j.clinph.2008.03.012","volume":"119","author":"K-Q Shen","year":"2008","unstructured":"Shen, K.-Q., Li, X.-P., Ong, C.-J., Shao, S.-Y., Wilder-Smith, E.P.V.: EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate. Clin. Neurophysiol. 119(7), 1524\u20131533 (2008)","journal-title":"Clin. Neurophysiol."},{"issue":"5","key":"11_CR24","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.humov.2010.08.010","volume":"30","author":"D Janssen","year":"2011","unstructured":"Janssen, D., Sch\u00f6llhorn, W.I., Newell, K.M., J\u00e4ger, J.M., Rost, F., Vehof, K.: Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps. Hum. Mov. Sci. 30(5), 966\u2013975 (2011)","journal-title":"Hum. Mov. Sci."},{"key":"11_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100575","volume":"24","author":"H Qu","year":"2021","unstructured":"Qu, H., Gao, X., Pang, L.: Classification of mental workload based on multiple features of ECG signals. Inform. Med. Unlocked 24, 100575 (2021)","journal-title":"Inform. Med. Unlocked"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Esener, I.I.: Subspace-based feature extraction on multi-physiological measurements of automobile drivers for distress recognition. Biomed. Sig. Process. Control 66, 102504 (2021)","DOI":"10.1016\/j.bspc.2021.102504"},{"key":"11_CR27","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.aap.2017.10.014","volume":"126","author":"E Molina","year":"2019","unstructured":"Molina, E., Sanabria, D., Jung, T.-P., Correa, \u00c1.: Electroencephalographic and peripheral temperature dynamics during a prolonged psychomotor vigilance task. Accid. Anal. Prev. 126, 198\u2013208 (2019)","journal-title":"Accid. Anal. Prev."},{"key":"11_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103360","volume":"72","author":"C Ye","year":"2022","unstructured":"Ye, C., Yin, Z., Zhao, M., Tian, Y., Sun, Z.: Identification of mental fatigue levels in a language understanding task based on multi-domain EEG features and an ensemble convolutional neural network. Biomed. Signal Process. Control 72, 103360 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"11_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110394","volume":"226","author":"I Konstantzos","year":"2020","unstructured":"Konstantzos, I., Sadeghi, S.A., Kim, M., Xiong, J., Tzempelikos, A.: The effect of lighting environment on task performance in buildings \u2013 a review. Energy Buildings 226, 110394 (2020)","journal-title":"Energy Buildings"},{"issue":"9","key":"11_CR30","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.ergon.2005.03.002","volume":"35","author":"H Jusl\u00e9n","year":"2005","unstructured":"Jusl\u00e9n, H., Tenner, A.: Mechanisms involved in enhancing human performance by changing the lighting in the industrial workplace. Int. J. Ind. Ergon. 35(9), 843\u2013855 (2005)","journal-title":"Int. J. Ind. Ergon."}],"container-title":["Lecture Notes in Computer Science","Distributed, Ambient and Pervasive Interactions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60012-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T23:08:07Z","timestamp":1718406487000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60012-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031600111","9783031600128"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60012-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}