{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:50:46Z","timestamp":1742964646757,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030582845"},{"type":"electronic","value":"9783030582852"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-58285-2_32","type":"book-chapter","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T09:00:01Z","timestamp":1599728401000},"page":"343-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Firefighter Virtual Reality Simulation for Personalized Stress Detection"],"prefix":"10.1007","author":[{"given":"Soeren","family":"Klingner","sequence":"first","affiliation":[]},{"given":"Zhiwei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Yuanting","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Bashar","family":"Altakrouri","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Michel","sequence":"additional","affiliation":[]},{"given":"Jonas","family":"Weiss","sequence":"additional","affiliation":[]},{"given":"Arvind","family":"Sridhar","sequence":"additional","affiliation":[]},{"given":"Sophie Mai","family":"Chau","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"issue":"5","key":"32_CR1","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1037\/a0033559","volume":"81","author":"PL Anderson","year":"2013","unstructured":"Anderson, P.L., et al.: Virtual reality exposure therapy for social anxiety disorder: a randomized controlled trial. J. Consult. Clin. Psychol. 81(5), 751 (2013)","journal-title":"J. Consult. Clin. Psychol."},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/0001-6918(66)90004-7","volume":"25","author":"AR Jensen","year":"1966","unstructured":"Jensen, A.R., Rohwer Jr., W.D.: The stroop color-word test: a review. Acta Psychol. 25, 36\u201393 (1966)","journal-title":"Acta Psychol."},{"issue":"1\u20132","key":"32_CR3","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1159\/000119004","volume":"28","author":"C Kirschbaum","year":"1993","unstructured":"Kirschbaum, C., Pirke, K.M., Hellhammer, D.H.: The \u2018trier social stress test\u2019-a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 28(1\u20132), 76\u201381 (1993)","journal-title":"Neuropsychobiology"},{"doi-asserted-by":"crossref","unstructured":"Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M.A., Kraaij, W.: The swell knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 291\u2013298 (2014)","key":"32_CR4","DOI":"10.1145\/2663204.2663257"},{"issue":"1","key":"32_CR5","doi-asserted-by":"publisher","first-page":"46","DOI":"10.7453\/gahmj.2014.073","volume":"4","author":"R McCraty","year":"2015","unstructured":"McCraty, R., Shaffer, F.: Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Adv. Health Med. 4(1), 46\u201361 (2015)","journal-title":"Global Adv. Health Med."},{"doi-asserted-by":"crossref","unstructured":"Oskooei, A., Chau, S.M., Weiss, J., Sridhar, A., Mart\u00ednez, M.R., Michel, B.: Destress: deep learning for unsupervised identification of mental stress in firefighters from heart-rate variability (hrv) data. arXiv preprint arXiv:1911.13213 (2019)","key":"32_CR6","DOI":"10.1007\/978-3-030-53352-6_9"},{"issue":"3","key":"32_CR7","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.jbtep.2007.07.007","volume":"39","author":"TD Parsons","year":"2008","unstructured":"Parsons, T.D., Rizzo, A.A.: Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis. J. Behav. Ther. Exp. Psychiatry 39(3), 250\u2013261 (2008)","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"doi-asserted-by":"crossref","unstructured":"Pluntke, U., Gerke, S., Sridhar, A., Weiss, J., Michel, B.: Evaluation and classification of physical and psychological stress in firefighters using heart rate variability. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2207\u20132212. IEEE (2019)","key":"32_CR8","DOI":"10.1109\/EMBC.2019.8856596"},{"issue":"1","key":"32_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.newideapsych.2009.11.002","volume":"29","author":"G Riva","year":"2011","unstructured":"Riva, G., Waterworth, J.A., Waterworth, E.L., Mantovani, F.: From intention to action: the role of presence. New Ideas Psychol. 29(1), 24\u201337 (2011)","journal-title":"New Ideas Psychol."},{"issue":"2","key":"32_CR10","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1023\/A:1024772308758","volume":"12","author":"BO Rothbaum","year":"1999","unstructured":"Rothbaum, B.O., et al.: Virtual reality exposure therapy for PTSD Vietnam veterans: case study. J. Traumatic Stress Off. Publ. Int. Soc. Traumatic Stress Stud. 12(2), 263\u2013271 (1999)","journal-title":"J. Traumatic Stress Off. Publ. Int. Soc. Traumatic Stress Stud."},{"doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400\u2013408 (2018)","key":"32_CR11","DOI":"10.1145\/3242969.3242985"},{"unstructured":"Sierro, N.: Firefighter vital sign monitoring for predicting operational readiness. EPFL Master thesisa (2020)","key":"32_CR12"},{"unstructured":"Weil, A.: Three breathing exercises. Retrieved 15 May 2017 (2016)","key":"32_CR13"},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.psyneuen.2018.11.010","volume":"101","author":"P Zimmer","year":"2019","unstructured":"Zimmer, P., Buttlar, B., Halbeisen, G., Walther, E., Domes, G.: Virtually stressed? a refined virtual reality adaptation of the trier social stress test (TSST) induces robust endocrine responses. Psychoneuroendocrinology 101, 186\u2013192 (2019)","journal-title":"Psychoneuroendocrinology"}],"container-title":["Lecture Notes in Computer Science","KI 2020: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58285-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T16:33:56Z","timestamp":1617208436000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-58285-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030582845","9783030582852"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58285-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"9 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bamberg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ki2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ki2020.uni-bamberg.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","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":"16","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":"12","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":"26% - 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":"3","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":"3","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)"}},{"value":"Due to the Corona pandemic KI 2020 was held as a virtual event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}