{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:02:45Z","timestamp":1742994165468,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031382765"},{"type":"electronic","value":"9783031382772"}],"license":[{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"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\/978-3-031-38277-2_20","type":"book-chapter","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T20:01:52Z","timestamp":1699041712000},"page":"241-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling Physical Fatigue Through Physiological Monitoring Within High-Risk Professions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4942-7625","authenticated-orcid":false,"given":"Denisse","family":"Bustos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7829-5222","authenticated-orcid":false,"given":"Filipa","family":"Cardoso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9722-3910","authenticated-orcid":false,"given":"Ricardo","family":"Cardoso","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2367-2187","authenticated-orcid":false,"given":"Joana","family":"Guedes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3947-8688","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Costa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6347-9608","authenticated-orcid":false,"given":"M\u00e1rio","family":"Vaz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-5503","authenticated-orcid":false,"given":"J. Santos","family":"Baptista","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5811-0443","authenticated-orcid":false,"given":"Ricardo J.","family":"Fernandes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,4]]},"reference":[{"issue":"19","key":"20_CR1","doi-asserted-by":"publisher","first-page":"5479","DOI":"10.1080\/10106049.2021.1920636","volume":"37","author":"R Abedi","year":"2022","unstructured":"Abedi, R., Costache, R., Shafizadeh-Moghadam, H., Pham, Q.B.: Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int. 37(19), 5479\u20135496 (2022). https:\/\/doi.org\/10.1080\/10106049.2021.1920636","journal-title":"Geocarto Int."},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Aguirre, A., Pinto, M.J., Cifuentes, C.A., Perdomo, O., D\u00edaz, C.A.R., M\u00fanera, M.: Machine learning approach for fatigue estimation in sit-to-stand exercise. Sensors 21(15) (2021)","DOI":"10.3390\/s21155006"},{"issue":"1","key":"20_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1177\/1754337118775548","volume":"233","author":"S Ameli","year":"2018","unstructured":"Ameli, S., Naghdy, F., Stirling, D., Naghdy, G., Aghmesheh, M.: Quantitative and non-invasive measurement of exercise-induced fatigue. Proc. Inst. Mech. Eng. Part P: J. Sport. Eng. Technol. 233(1), 34\u201345 (2018). https:\/\/doi.org\/10.1177\/1754337118775548","journal-title":"Proc. Inst. Mech. Eng. Part P: J. Sport. Eng. Technol."},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Anwer, S., Li, H., Antwi-Afari, M.F., Umer, W., Wong, A.Y.L. (2021). Evaluation of physiological metrics as real-time measurement of physical fatigue in construction workers: state-of-the-art review [Review]. J. Constr. Eng. Manag. 147(5). Article 03121001. https:\/\/doi.org\/10.1061\/(ASCE)CO.1943-7862.0002038","DOI":"10.1061\/(ASCE)CO.1943-7862.0002038"},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Aryal, A., Ghahramani, A., Becerik-Gerber, B.: Monitoring fatigue in construction workers using physiological measurements. Autom. Constr. 82, 154\u2013165 (2017). https:\/\/doi.org\/10.1016\/j.autcon.2017.03.003","DOI":"10.1016\/j.autcon.2017.03.003"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s40279-022-01748-2","volume":"53","author":"M Behrens","year":"2023","unstructured":"Behrens, M., Gube, M., Chaabene, H., Prieske, O., Zenon, A., Broscheid, K.C., Schega, L., Husmann, F., Weippert, M.: Fatigue and human performance: an updated framework. Sport. Med. 53(1), 7\u201331 (2023). https:\/\/doi.org\/10.1007\/s40279-022-01748-2","journal-title":"Sport. Med."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Bongers, C.C.W.G., Daanen, H.A.M., Bogerd, C.P., Hopman, M.T.E., Eijsvogels, T.M.H.: Validity, reliability, and inertia of four different temperature capsule systems. Med. Sci. Sport. Exerc. 50(1) (2018). https:\/\/journals.lww.com\/acsm-msse\/Fulltext\/2018\/01000\/Validity,_Reliability,_and_Inertia_of_Four.21.aspx","DOI":"10.1249\/MSS.0000000000001403"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Bustos, D., Cardoso, F., Rios, M., Vaz, M., Guedes, J., Torres Costa, J., Santos Baptista, J., Fernandes, R.J.: Machine learning approach to model physical fatigue during incremental exercise among firefighters. Sensors 23(1) (2023). https:\/\/doi.org\/10.3390\/s23010194","DOI":"10.3390\/s23010194"},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Bustos, D., Guedes, J., Alvares, M., Vaz, M., Torres Costa, J.: Real time fatigue assessment: identification and continuous tracing of fatigue using a physiological assessment algorithm. Occup. Environ. Saf. Health (2019). In Press. https:\/\/doi.org\/10.1007\/978-3-030-14730-3_28","DOI":"10.1007\/978-3-030-14730-3_28"},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Bustos, D., Guedes, J., Vaz, M., Pombo, E., Fernandes, R.J., Torres Costa, J., Santos Baptista, J.: Non-invasive physiological monitoring for physical exertion and fatigue assessment in military personnel: a systematic review. Int. J. Environ. Res. Public Health 18(16) (2021). https:\/\/doi.org\/10.3390\/ijerph18168815","DOI":"10.3390\/ijerph18168815"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Bustos, D., Guedes, J.C., Vaz, M., Costa, J.T., Fernandes, R.J., Santos Baptista, J.: Fatigue assessment through physiological monitoring during march-run series: preliminary results. In: Arezes, P.M., Baptista, J.S., Carneiro, P., Castelo Branco, J., Costa, N., Duarte, J., Guedes, J.C., Melo, R.B., Miguel, A.S., Perestrelo, G. (eds.) Occupational and Environmental Safety and Health III, pp. 307\u2013319. Springer International Publishing (2022). https:\/\/doi.org\/10.1007\/978-3-030-89617-1_28","DOI":"10.1007\/978-3-030-89617-1_28"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Cardoso, F., Monteiro, A.S., Vilas-Boas, J.P., Pinho, J.C., Pyne, D.B., Fernandes, R.J.: Effects of wearing a 50% lower jaw advancement splint on biophysical and perceptual responses at low to severe running intensities. Life 12(2) (2022). https:\/\/doi.org\/10.3390\/life12020253","DOI":"10.3390\/life12020253"},{"key":"20_CR13","doi-asserted-by":"publisher","unstructured":"Farag, A., Scott, L.D., Perkhounkova, Y., Saeidzadeh, S., Hein, M.: A human factors approach to evaluate predictors of acute care nurse occupational fatigue. Appl. Ergon. 100, 103647 (2022). https:\/\/doi.org\/10.1016\/j.apergo.2021.103647","DOI":"10.1016\/j.apergo.2021.103647"},{"key":"20_CR14","doi-asserted-by":"publisher","unstructured":"Friedl, K.E.: Military applications of soldier physiological monitoring. J. Sci. Med. Sport. 21(11), 1147\u20131153 (2018). https:\/\/doi.org\/10.1016\/j.jsams.2018.06.004","DOI":"10.1016\/j.jsams.2018.06.004"},{"key":"20_CR15","doi-asserted-by":"publisher","unstructured":"Geuzinge, R., Visse, M., Duyndam, J., Vermetten, E.: Social embeddedness of firefighters, paramedics, specialized nurses, police officers, and military personnel: systematic review in relation to the risk of traumatization [systematic review]. Front. Psych. 11(2020). https:\/\/doi.org\/10.3389\/fpsyt.2020.496663","DOI":"10.3389\/fpsyt.2020.496663"},{"key":"20_CR16","unstructured":"Guedes, J.C., Costa, E.Q., Baptista, J.S.: Using a climatic chamber to measure the human psychophysiological response under different combinations of temperature and humidity. Thermol. Int. 22, 49\u201354 (2012). http:\/\/ww.uhlen.at\/thermology-international\/archive\/EAT2012_Book_of_Proceedings.pdf#page=50"},{"key":"20_CR17","doi-asserted-by":"publisher","unstructured":"Halson, S.L.: Monitoring training load to understand fatigue in athletes. Sport. Med. 44(Suppl. 2), S139\u2013147 (2014). https:\/\/doi.org\/10.1007\/s40279-014-0253-z","DOI":"10.1007\/s40279-014-0253-z"},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Hooda, R., Joshi, V., Shah, M.: A comprehensive review of approaches to detect fatigue using machine learning techniques. Chronic Dis. Transl. Med. (2021). https:\/\/doi.org\/10.1016\/j.cdtm.2021.07.002","DOI":"10.1016\/j.cdtm.2021.07.002"},{"key":"20_CR19","unstructured":"ISO: Ergonomics-Evaluation of Thermal Strain by Physiological Measurements. ISO (2004)"},{"key":"20_CR20","doi-asserted-by":"publisher","unstructured":"Jebelli, H., Choi, B., Lee, S.: Application of wearable biosensors to construction sites. II: assessing workers\u2019 physical demand. J. Constr. Eng. Manag. 145(12), 04019080 (2019). https:\/\/doi.org\/10.1061\/(ASCE)CO.1943-7862.0001710","DOI":"10.1061\/(ASCE)CO.1943-7862.0001710"},{"key":"20_CR21","doi-asserted-by":"publisher","unstructured":"Kang, M., Jameson, N.J.: Machine learning: fundamentals. In: Prognostics and Health Management of Electronics, pp. 85\u2013109 (2018). https:\/\/doi.org\/10.1002\/9781119515326.ch4","DOI":"10.1002\/9781119515326.ch4"},{"key":"20_CR22","doi-asserted-by":"publisher","unstructured":"Knoop, V., Cloots, B., Costenoble, A., Debain, A., Vella Azzopardi, R., Vermeiren, S., Jansen, B., Scafoglieri, A., Bautmans, I., Bautmans, I., Vert\u00e9, D., Beyer, I., Petrovic, M., De Donder, L., Kardol, T., Rossi, G., Clarys, P., Scafoglieri, A., Cattrysse, E., de Hert, P., Jansen, B.: Fatigue and the prediction of negative health outcomes: a systematic review with meta-analysis. Ageing Res. Rev. 67, 101261 (2021). https:\/\/doi.org\/10.1016\/j.arr.2021.101261","DOI":"10.1016\/j.arr.2021.101261"},{"key":"20_CR23","doi-asserted-by":"publisher","unstructured":"Lee, W., Lin, K.-Y., Seto, E., Migliaccio, G.C.: Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction. Autom. Constr. 83, 341\u2013353 (2017). https:\/\/doi.org\/10.1016\/j.autcon.2017.06.012","DOI":"10.1016\/j.autcon.2017.06.012"},{"issue":"2","key":"20_CR24","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1097\/JOM.0b013e318247a3b0","volume":"54","author":"SE Lerman","year":"2012","unstructured":"Lerman, S.E., Eskin, E., Flower, D.J., George, E.C., Gerson, B., Hartenbaum, N., Hursh, S.R., Moore-Ede, M.: Fatigue risk management in the workplace. J. Occup. Environ. Med. 54(2), 231\u2013258 (2012)","journal-title":"J. Occup. Environ. Med."},{"issue":"4","key":"20_CR25","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.biopsych.2004.11.014","volume":"57","author":"HR Lieberman","year":"2005","unstructured":"Lieberman, H.R., Bathalon, G.P., Falco, C.M., Kramer, F.M., Morgan, C.A., Niro, P.: Severe decrements in cognition function and mood induced by sleep loss, heat, dehydration, and undernutrition during simulated combat. Biol. Psychiat. 57(4), 422\u2013429 (2005)","journal-title":"Biol. Psychiat."},{"key":"20_CR26","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.physbeh.2016.06.037","volume":"165","author":"HR Lieberman","year":"2016","unstructured":"Lieberman, H.R., Farina, E.K., Caldwell, J., Williams, K.W., Thompson, L.A., Niro, P.J., Grohmann, J.A., McClung, J.P.: Cognitive function, stress hormones, heart rate and nutritional status during simulated captivity in military survival training. Physiol. Behav. 165, 86\u201397 (2016)","journal-title":"Physiol. Behav."},{"key":"20_CR27","doi-asserted-by":"publisher","unstructured":"Rachmawati, S., Aktsari, M., Suryaningsih, A., Hawali Abdul Matin, H., Suryadi, I.: Assessment work fatigue to workers in environment underground mining areas based on fatigue assessment scale questionnaires. In: E3S Web Conference, p. 202 (2020). https:\/\/doi.org\/10.1051\/e3sconf\/202020205013","DOI":"10.1051\/e3sconf\/202020205013"},{"key":"20_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijpsycho.2017.04.002","volume":"117","author":"CS Ralph","year":"2017","unstructured":"Ralph, C.S., Vartanian, O., Lieberman, H.R., Morgan, C.A., Cheung, B.: The effects of captivity survival training on mood, dissociation, PTSD symptoms, cognitive performance and stress hormones. Int. J. Psychophysiol. 117, 37\u201347 (2017)","journal-title":"Int. J. Psychophysiol."},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Ray, S.: A quick review of machine learning algorithms. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (14\u201316 Feb 2019)","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"20_CR30","doi-asserted-by":"publisher","unstructured":"Sedighi Maman, Z., Chen, Y.-J., Baghdadi, A., Lombardo, S., Cavuoto, L.A., Megahed, F.M.: A data analytic framework for physical fatigue management using wearable sensors. Expert. Syst. Appl. 155, 113405 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113405","DOI":"10.1016\/j.eswa.2020.113405"},{"key":"20_CR31","doi-asserted-by":"publisher","unstructured":"Soucy, H., Arcidiacono, D., Sutton, A., Potter, A., Pitts, K., Santee, W., Looney, D.: Physiological considerations for modern military rifle carriage. J. Sport. Hum. Perform. 11(1), 1\u201312 (2023). https:\/\/doi.org\/10.12922\/jshp.v11i1.188","DOI":"10.12922\/jshp.v11i1.188"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Sousa, A.N.A., Figueiredo, P., Zamparo, P., Pyne, D.B., Vilas-Boas, J.P., Fernandes, R.J.: Exercise modality effect on bioenergetical performance at V\u02d9O2max intensity. Med. Sci. Sport. Exerc. 47(8) (2015). https:\/\/journals.lww.com\/acsm-msse\/Fulltext\/2015\/08000\/Exercise_Modality_Effect_on_Bioenergetical.19.aspx","DOI":"10.1249\/MSS.0000000000000580"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Stephenson, M.D., Thompson, A.G., Merrigan, J.J., Stone, J.D., Hagen, J.A.: Applying heart rate variability to monitor health and performance in tactical personnel: a narrative review. Int. J. Environ. Res. Public Health 18(15) (2021)","DOI":"10.3390\/ijerph18158143"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Techera, U., Hallowell, M., Stambaugh, N., Littlejohn, R.: Causes and consequences of occupational fatigue: meta-analysis and systems model. J. Occup. Environ. Med. 58(10) (2016). https:\/\/journals.lww.com\/joem\/Fulltext\/2016\/10000\/Causes_and_Consequences_of_Occupational_Fatigue_.1.aspx","DOI":"10.1097\/JOM.0000000000000837"},{"key":"20_CR35","doi-asserted-by":"publisher","unstructured":"Umer, W., Li, H., Yantao, Y., Antwi-Afari, M.F., Anwer, S., Luo, X.: Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures. Autom. Constr. 112, 103079 (2020). https:\/\/doi.org\/10.1016\/j.autcon.2020.103079","DOI":"10.1016\/j.autcon.2020.103079"},{"key":"20_CR36","doi-asserted-by":"publisher","unstructured":"Williamson, A., Lombardi, D.A., Folkard, S., Stutts, J., Courtney, T.K., Connor, J.L.: The link between fatigue and safety. Accid. Anal. Prev. 43(2), 498\u2013515 (2011). https:\/\/doi.org\/10.1016\/j.aap.2009.11.011","DOI":"10.1016\/j.aap.2009.11.011"},{"key":"20_CR37","doi-asserted-by":"publisher","unstructured":"Yook, Y.-S.: Firefighters\u2019 occupational stress and its correlations with cardiorespiratory fitness, arterial stiffness, heart rate variability, and sleep quality. PLoS One 14(12), e0226739 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0226739","DOI":"10.1371\/journal.pone.0226739"},{"key":"20_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, M., Sparer, E.H., Murphy, L.A., Dennerlein, J.T., Fang, D., Katz, J.N., Caban-Martinez, A.J.: Development and validation of a fatigue assessment scale for U.S. construction workers. Am. J. Ind. Med. 58(2), 220\u2013228 (2015). https:\/\/doi.org\/10.1002\/ajim.22411","DOI":"10.1002\/ajim.22411"}],"container-title":["Studies in Systems, Decision and Control","Occupational and Environmental Safety and Health V"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-38277-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T20:07:48Z","timestamp":1699042068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-38277-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"ISBN":["9783031382765","9783031382772"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-38277-2_20","relation":{},"ISSN":["2198-4182","2198-4190"],"issn-type":[{"type":"print","value":"2198-4182"},{"type":"electronic","value":"2198-4190"}],"subject":[],"published":{"date-parts":[[2023,11,4]]},"assertion":[{"value":"4 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}