{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:19:23Z","timestamp":1726042763309},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030279493"},{"type":"electronic","value":"9783030279509"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-27950-9_2","type":"book-chapter","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T05:02:56Z","timestamp":1566882176000},"page":"23-38","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bio-behavioral Modeling of Workload and Performance"],"prefix":"10.1007","author":[{"given":"Jean-Fran\u00e7ois","family":"Gagnon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivier","family":"Gagnon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Lafond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Parent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Tremblay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,14]]},"reference":[{"key":"2_CR1","unstructured":"Carter, R., Cheuvront, S.N., Sawka, M.N.: Operator Functional State Assessment (l\u2019\u00e9valuation de l\u2019aptitude op\u00e9rationnelle de l\u2019op\u00e9rateur humain). Army research institute (2004)"},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1177\/1541931214581199","volume":"58","author":"Bethany K. Bracken","year":"2014","unstructured":"Bracken, B.K., Palmon, N., Romero, V., Pfautz, J., Cooke, N.J.: A prototype toolkit for sensing and modeling individual and team state. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 58, pp. 949\u2013953 (2014). https:\/\/doi.org\/10.1177\/1541931214581199","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Durkee, K.T., Pappada, S.M., Ortiz, A.E., Feeney, J.J., Galster, S.M.: System decision framework for augmenting human performance using real-time workload classifiers. Presented at the 2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), Orlando, FL (2015)","DOI":"10.1109\/COGSIMA.2015.7107968"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.cmpb.2013.09.007","volume":"113","author":"Z Yin","year":"2014","unstructured":"Yin, Z., Zhang, J.: Operator functional state classification using least-square support vector machine based recursive feature elimination technique. Comput. Methods Programs Biomed. 113, 101\u2013115 (2014). https:\/\/doi.org\/10.1016\/j.cmpb.2013.09.007","journal-title":"Comput. Methods Programs Biomed."},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.bbr.2013.10.042","volume":"259","author":"G Durantin","year":"2014","unstructured":"Durantin, G., Gagnon, J.-F., Tremblay, S., Dehais, F.: Using near infrared spectroscopy and heart rate variability to detect mental overload. Behav. Brain Res. 259, 16\u201323 (2014). https:\/\/doi.org\/10.1016\/j.bbr.2013.10.042","journal-title":"Behav. Brain Res."},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Hogervorst, M.A., Brouwer, A.-M., van Erp, J.B.F.: Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Front. Neurosci. 8 (2014). https:\/\/doi.org\/10.3389\/fnins.2014.00322","DOI":"10.3389\/fnins.2014.00322"},{"key":"2_CR7","first-page":"1","volume":"99","author":"DV Tobon","year":"2014","unstructured":"Tobon, D.V., Falk, T., Maier, M.: MS-QI: a modulation spectrum-based ECG quality index for telehealth applications. IEEE Trans. Biomed. Eng. 99, 1 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.biopsycho.2014.02.006","volume":"99","author":"TJ Overbeek","year":"2014","unstructured":"Overbeek, T.J., van Boxtel, A., Westerink, J.H.: Respiratory sinus arrhythmia responses to cognitive tasks: effects of task factors and RSA indices. Biol. Psychol. 99, 1\u201314 (2014)","journal-title":"Biol. Psychol."},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Wijsman, J., Grundlehner, B., Liu, H., Penders, J., Hermens, H.: Wearable physiological sensors reflect mental stress state in office-like situations. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp. 600\u2013605 (2013). https:\/\/doi.org\/10.1109\/acii.2013.105","DOI":"10.1109\/acii.2013.105"},{"issue":"89","key":"2_CR10","doi-asserted-by":"publisher","first-page":"20130719","DOI":"10.1098\/rsif.2013.0719","volume":"10","author":"Aaron Williamon","year":"2013","unstructured":"Williamon, A., Aufegger, L., Wasley, D., Looney, D., Mandic, D.P.: Complexity of physiological responses decreases in high-stress musical performance. J. R. Soc. Interface 10 (2013). https:\/\/doi.org\/10.1098\/rsif.2013.0719","journal-title":"Journal of The Royal Society Interface"},{"issue":"3","key":"2_CR11","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/THMS.2014.2307258","volume":"44","author":"N R\u00e9gis","year":"2014","unstructured":"R\u00e9gis, N., et al.: Formal detection of attentional tunneling in human operator-automation interactions. IEEE Trans. Hum. Mach. Syst. 44(3), 326\u2013336 (2014)","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1177\/0018720814539505","volume":"57","author":"G Matthews","year":"2015","unstructured":"Matthews, G., Reinerman-Jones, L.E., Barber, D.J., Abich, J.: The psychometrics of mental workload multiple measures are sensitive but divergent. Hum. Factors J. Hum. Factors Ergon. Soc. 57, 125\u2013143 (2015). https:\/\/doi.org\/10.1177\/0018720814539505","journal-title":"Hum. Factors J. Hum. Factors Ergon. Soc."},{"key":"2_CR13","first-page":"24","volume":"355","author":"AW Gaillard","year":"2003","unstructured":"Gaillard, A.W.: Fatigue assessment and performance protection. NATO Sci. Ser. Sub Ser. I Life Behav. Sci. 355, 24\u201335 (2003)","journal-title":"NATO Sci. Ser. Sub Ser. I Life Behav. Sci."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Eggemeier, F.T., Wilson, G.F., Kramer, A.F., Damos, D.L.: Workload Assessment in Multi-Task Environments. Multiple-Task Performance, pp. 207\u2013216 (1991)","DOI":"10.1201\/9781003069447-12"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1518\/hfes.45.4.635.27088","volume":"45","author":"GF Wilson","year":"2003","unstructured":"Wilson, G.F., Russell, C.A.: Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Hum. Factors 45, 635\u2013643 (2003)","journal-title":"Hum. Factors"},{"key":"2_CR16","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-642-40483-2_11","volume-title":"Human-Computer Interaction\u2013INTERACT","author":"N Nourbakhsh","year":"2013","unstructured":"Nourbakhsh, N., Wang, Y., Chen, F.: GSR and blink features for cognitive load classification. In: Kotz\u00e9, P., Marsden, G., Lindgaard, G., Wesson, J., Winckler, M. (eds.) Human-Computer Interaction\u2013INTERACT. LNCS, vol. 8117, pp. 159\u2013166. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40483-2_11"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Brouwer, A.-M., Zander, T.O., van Erp, J.B.F., Korteling, J.E., Bronkhorst, A.W.: Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Front. Neurosci. 9 (2015). https:\/\/doi.org\/10.3389\/fnins.2015.00136","DOI":"10.3389\/fnins.2015.00136"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Gagnon, J.-F., Gagnon, O., Lafond, D., Parent, M., Tremblay, S.: A systematic assessment of operational metrics for modeling operator functional state: In: Proceedings of the 3rd International Conference on Physiological Computing Systems, pp. 15\u201323 SCITEPRESS - Science and Technology Publications, Lisbon, Portugal (2016). https:\/\/doi.org\/10.5220\/0005921600150023","DOI":"10.5220\/0005921600150023"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Poole, A., Ball, L.J.: Eye tracking in human-computer interaction and usability research: current status and future prospects. In: Encyclopedia of Human-Computer Interaction, pp. 211\u2013219 (2005). https:\/\/doi.org\/10.4018\/978-1-59140-562-7","DOI":"10.4018\/978-1-59140-562-7"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 71\u201378. ACM (2000)","DOI":"10.1145\/355017.355028"},{"key":"2_CR21","first-page":"85","volume":"2011","author":"S Boonnithi","year":"2011","unstructured":"Boonnithi, S., Phongsuphap, S.: Comparison of heart rate variability measures for mental stress detection. Comput. Cardiol. 2011, 85\u201388 (2011)","journal-title":"Comput. Cardiol."},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Li\u00f1ares, L., Vila, X., Mendez, A., Lado, M., Olivieri, D.: RHRV: an R-based software package for heart rate variability analysis of ECG recordings. In: 3rd Iberian Conference in Systems and Information Technologies (CISTI 2008), Vigo, Spain (2008)","DOI":"10.32614\/CRAN.package.RHRV"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Gagnon, O., Lafond, D., Gagnon, J-F., Parizeau, M.: Comparing methods for assessing operator functional state. In: Proceedings of the 2016 IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, CA, USA, 21\u201325 March 2016","DOI":"10.1109\/COGSIMA.2016.7497792"},{"key":"2_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-319-20816-9_10","volume-title":"Foundations of Augmented Cognition","author":"H Oh","year":"2015","unstructured":"Oh, H., et al.: A composite cognitive workload assessment system in pilots under various task demands using ensemble learning. In: Schmorrow, Dylan D., Fidopiastis, Cali M. (eds.) AC 2015. LNCS (LNAI), vol. 9183, pp. 91\u2013100. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20816-9_10"},{"key":"2_CR25","first-page":"05003","volume":"1","author":"M Kuhn","year":"2015","unstructured":"Kuhn, M.: caret: classification and regression training. Astrophys. Source Code Libr. 1, 05003 (2015)","journal-title":"Astrophys. Source Code Libr."},{"key":"2_CR26","unstructured":"Torgo, L.: Data Mining with R, Learning with Case Studies. Chapman and Hall\/CRC (2010). http:\/\/www.dcc.fc.up.pt\/~ltorgo\/DataMiningWithR"},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neuroimage.2011.07.094","volume":"59","author":"Z Wang","year":"2012","unstructured":"Wang, Z., Hope, R.M., Wang, Z., Ji, Q., Gray, W.D.: Cross-subject workload classification with a hierarchical Bayes model. NeuroImage Neuroergon. Hum. Brain Action Work 59, 64\u201369 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2011.07.094","journal-title":"NeuroImage Neuroergon. Hum. Brain Action Work"}],"container-title":["Lecture Notes in Computer Science","Physiological Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-27950-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T16:02:28Z","timestamp":1721664148000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-27950-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030279493","9783030279509"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-27950-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"14 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PhyCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Physiological Computing Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"phycs2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.fens.org\/News-Activities\/Calendar\/Meetings\/2016\/07\/PhyCS-2016---3rd-International-Conference-on-Physiological-Computing-Systems\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"N\/A% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}