74912 20200716101524.0 doi 10.1007/s11517-018-1879-z sideral 106983 ART-2019-106983 eng Arza, Adriana Measuring acute stress response through physiological signals: towards a quantitative assessment of stress 2019 Access copy available to the general public Unrestricted Social and medical problems associated with stress are increasing globally and seriously affect mental health and well-being. However, an effective stress-level monitoring method is still not available. This paper presents a quantitative method for monitoring acute stress levels in healthy young people using biomarkers from physiological signals that can be unobtrusively monitored. Two states were induced to 40 volunteers, a basal state generated with a relaxation task and an acute stress state generated by applying a standard stress test that includes five different tasks. Standard psychological questionnaires and biochemical markers were utilized as ground truth of stress levels. A multivariable approach to comprehensively measure the physiological stress response is proposed using stress biomarkers derived from skin temperature, heart rate, and pulse wave signals. Acute physiological stress levels (total-range 0–100 au) were continuously estimated every 1 min showing medians of 29.06 au in the relaxation tasks, while rising from 34.58 to 47.55 au in the stress tasks. Moreover, using the proposed method, five statistically different stress levels induced by the performed tasks were also measured. Results obtained show that, in these experimental conditions, stress can be monitored from unobtrusive biomarkers. Thus, a more general stress monitoring method could be derived based on this approach. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.022 2019 MATHEMATICAL & COMPUTATIONAL BIOLOGY 24 / 59 = 0.407 2019 Q2 T2 MEDICAL INFORMATICS 18 / 27 = 0.667 2019 Q3 T3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 61 / 109 = 0.56 2019 Q3 T2 ENGINEERING, BIOMEDICAL 58 / 87 = 0.667 2019 Q3 T3 0.552 2019 Computer Science Applications 2019 Q2 Biomedical Engineering 2019 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Garzón-Rey, Jorge Mario (orcid)0000-0001-8742-0072 Lázaro, Jesús Universidad de Zaragoza (orcid)0000-0001-7285-0715 Gil, Eduardo Universidad de Zaragoza (orcid)0000-0002-3360-7015 Lopez-Anton, Raul Universidad de Zaragoza (orcid)0000-0003-2284-7862 de la Camara, Conchita Universidad de Zaragoza (orcid)0000-0003-3434-9254 Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-1272-0550 Bailon, Raquel Universidad de Zaragoza Aguiló, Jordi 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 4009 730 Universidad de Zaragoza Dpto. Psicología y Sociología Área Psicología Básica 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 1007 745 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Psiquiatría 57, 1 (2019), 271-287 Med. biol. eng. comput. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 0140-0118 1177723 http://zaguan.unizar.es/record/74912/files/texto_completo.pdf Postprint 80092 http://zaguan.unizar.es/record/74912/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:74912 articulos driver 2020-07-16-09:29:04 ARTICLE