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A one-class-classification approach to create a stresslevel curve plotter through wearable measurements and behavioral patterns
Occupational stress has become an interesting field of research in recent years. Stress in students may yield a decline in academic performance or an increase of a mental issue, hence making of paramount importance the timely diagnosis of stress. Although there exist mechanisms for inferring stress...
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Published in: | International journal on interactive design and manufacturing 2021-09, Vol.15 (2-3), p.159-171 |
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container_title | International journal on interactive design and manufacturing |
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creator | Ramírez-Valenzuela, Rodolfo A. Monroy, Raúl Loyola-González, Octavio Godínez, Fernando Soberanes-Martín, Anabelem |
description | Occupational stress has become an interesting field of research in recent years. Stress in students may yield a decline in academic performance or an increase of a mental issue, hence making of paramount importance the timely diagnosis of stress. Although there exist mechanisms for inferring stress level, most of them: assume the test subject is in a controlled environment; use uncomfortable or unaffordable sensors; or they are applicable only when the subject is at a particular posture. Moreover, to the best of the authors’ knowledge, there is no method capable of plotting a person’s stress level curve on the fly. In this paper, we propose a method capable of doing so; our method combines a set of one-class-classifiers capable of capturing the user stress level according to four strata (Low, Medium–Low, Medium–High, and High). Throughout our research, we have developed a dataset, called
Student Resilience
, which contains observation of several test subjects carrying a mobile phone, and wearing a wristband. For each test subject our dataset also contains the output of a collection of tests, especially designed to evaluate mental health and self-perceived stress. We have used the survey output as ground truth for validation purposes. Our method was capable of correctly plotting stress for 87% of the days submitted by the test subjects. Additionally, in a further attempt to validate our method, we have used data mining to determine whether a stress plot is likely to be explained by the unique activities carried out by each test subject for a given day of the week. |
doi_str_mv | 10.1007/s12008-021-00765-1 |
format | article |
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Student Resilience
, which contains observation of several test subjects carrying a mobile phone, and wearing a wristband. For each test subject our dataset also contains the output of a collection of tests, especially designed to evaluate mental health and self-perceived stress. We have used the survey output as ground truth for validation purposes. Our method was capable of correctly plotting stress for 87% of the days submitted by the test subjects. Additionally, in a further attempt to validate our method, we have used data mining to determine whether a stress plot is likely to be explained by the unique activities carried out by each test subject for a given day of the week.</description><identifier>ISSN: 1955-2513</identifier><identifier>EISSN: 1955-2505</identifier><identifier>DOI: 10.1007/s12008-021-00765-1</identifier><language>eng</language><publisher>Paris: Springer Paris</publisher><subject>Academic achievement ; CAE) and Design ; Camcorders ; Computer-Aided Engineering (CAD ; Data mining ; Datasets ; Electronics and Microelectronics ; Engineering ; Engineering Design ; Industrial Design ; Instrumentation ; Learning ; Mechanical Engineering ; Mental health ; Original Paper ; Physiology ; Plotting ; Psychological stress ; Reading ; Sensors ; Stress ; Students</subject><ispartof>International journal on interactive design and manufacturing, 2021-09, Vol.15 (2-3), p.159-171</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-4dd6959a5bfdcacd6643c664519e64945e8a32e67c7728bb49f07b7f9791ac623</cites><orcidid>0000-0002-6910-5922</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Ramírez-Valenzuela, Rodolfo A.</creatorcontrib><creatorcontrib>Monroy, Raúl</creatorcontrib><creatorcontrib>Loyola-González, Octavio</creatorcontrib><creatorcontrib>Godínez, Fernando</creatorcontrib><creatorcontrib>Soberanes-Martín, Anabelem</creatorcontrib><title>A one-class-classification approach to create a stresslevel curve plotter through wearable measurements and behavioral patterns</title><title>International journal on interactive design and manufacturing</title><addtitle>Int J Interact Des Manuf</addtitle><description>Occupational stress has become an interesting field of research in recent years. Stress in students may yield a decline in academic performance or an increase of a mental issue, hence making of paramount importance the timely diagnosis of stress. Although there exist mechanisms for inferring stress level, most of them: assume the test subject is in a controlled environment; use uncomfortable or unaffordable sensors; or they are applicable only when the subject is at a particular posture. Moreover, to the best of the authors’ knowledge, there is no method capable of plotting a person’s stress level curve on the fly. In this paper, we propose a method capable of doing so; our method combines a set of one-class-classifiers capable of capturing the user stress level according to four strata (Low, Medium–Low, Medium–High, and High). Throughout our research, we have developed a dataset, called
Student Resilience
, which contains observation of several test subjects carrying a mobile phone, and wearing a wristband. For each test subject our dataset also contains the output of a collection of tests, especially designed to evaluate mental health and self-perceived stress. We have used the survey output as ground truth for validation purposes. Our method was capable of correctly plotting stress for 87% of the days submitted by the test subjects. Additionally, in a further attempt to validate our method, we have used data mining to determine whether a stress plot is likely to be explained by the unique activities carried out by each test subject for a given day of the week.</description><subject>Academic achievement</subject><subject>CAE) and Design</subject><subject>Camcorders</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Electronics and Microelectronics</subject><subject>Engineering</subject><subject>Engineering Design</subject><subject>Industrial Design</subject><subject>Instrumentation</subject><subject>Learning</subject><subject>Mechanical Engineering</subject><subject>Mental health</subject><subject>Original Paper</subject><subject>Physiology</subject><subject>Plotting</subject><subject>Psychological stress</subject><subject>Reading</subject><subject>Sensors</subject><subject>Stress</subject><subject>Students</subject><issn>1955-2513</issn><issn>1955-2505</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxDAUhYsoOI7-AVcB19UkbZJmOQy-YMCNrsNtejvt0Glqko648q9brejOzX3AOedyvyS5ZPSaUapuAuOUFinlLJ1WKVJ2lCyYFiLlgorj35llp8lZCDtKZUELukg-VsT1mNoOQphrW7cWYut6AsPgHdiGREesR4hIgIToMYQOD9gRO_oDkqFzMaInsfFu3DbkDcFD2SHZI4TR4x77GAj0FSmxgUPrPHRkgC9PH86Tkxq6gBc_fZm83N0-rx_SzdP943q1SS1XNKZ5VUktNIiyrizYSso8s1MRTKPMdS6wgIyjVFYpXpRlrmuqSlVrpRlYybNlcjXnTi-9jhii2bnR99NJwzXTWZZJKicVn1XWuxA81mbw7R78u2HUfIE2M2gzgTbfoA2bTNlsCpO436L_i_7H9QnTMYOB</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Ramírez-Valenzuela, Rodolfo A.</creator><creator>Monroy, Raúl</creator><creator>Loyola-González, Octavio</creator><creator>Godínez, Fernando</creator><creator>Soberanes-Martín, Anabelem</creator><general>Springer Paris</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-6910-5922</orcidid></search><sort><creationdate>20210901</creationdate><title>A one-class-classification approach to create a stresslevel curve plotter through wearable measurements and behavioral patterns</title><author>Ramírez-Valenzuela, Rodolfo A. ; 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Student Resilience
, which contains observation of several test subjects carrying a mobile phone, and wearing a wristband. For each test subject our dataset also contains the output of a collection of tests, especially designed to evaluate mental health and self-perceived stress. We have used the survey output as ground truth for validation purposes. Our method was capable of correctly plotting stress for 87% of the days submitted by the test subjects. Additionally, in a further attempt to validate our method, we have used data mining to determine whether a stress plot is likely to be explained by the unique activities carried out by each test subject for a given day of the week.</abstract><cop>Paris</cop><pub>Springer Paris</pub><doi>10.1007/s12008-021-00765-1</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-6910-5922</orcidid></addata></record> |
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subjects | Academic achievement CAE) and Design Camcorders Computer-Aided Engineering (CAD Data mining Datasets Electronics and Microelectronics Engineering Engineering Design Industrial Design Instrumentation Learning Mechanical Engineering Mental health Original Paper Physiology Plotting Psychological stress Reading Sensors Stress Students |
title | A one-class-classification approach to create a stresslevel curve plotter through wearable measurements and behavioral patterns |
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