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Reference Signal-Based Method to Remove Respiration Noise in Electrodermal Activity (EDA) Collected from the Field
  • Lee, Gaang ;
  • Choi, Byungjoo ;
  • Jebelli, Houtan ;
  • Ahn, Changbum Ryan ;
  • Lee, Sang Hyun
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dc.contributor.authorLee, Gaang-
dc.contributor.authorChoi, Byungjoo-
dc.contributor.authorJebelli, Houtan-
dc.contributor.authorAhn, Changbum Ryan-
dc.contributor.authorLee, Sang Hyun-
dc.date.issued2019-01-01-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/36400-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078116014&origin=inward-
dc.description.abstractMeasuring built environment users' response using wearable biosensors could provide a new opportunity for understanding their experience in built environment. Electrodermal activity (EDA) sensors are especially useful in detecting people's stressful interaction with the built environment. Despite this potential advancement, the detection accuracy is still limited because of noises in EDA collected from uncontrolled settings. Alleviating respiration noise is most challenging due to the similarity in signal characteristics between the respiration noise and EDA response to distress. The authors propose an adaptive denoising method that references photoplethysmogram (PPG) to detect and remove respiration noise in EDA. Quality of denoising and quality improvement in stress measurement were measured for validation. The results showed that the proposed method brought better quality of respiration noise removal than previous methods, and therefore improved stress measurement quality. The finding can contribute to improve quality of EDA from the field, which is essential to accurately understand people's stressful interaction with built environment.-
dc.description.sponsorshipThis study was supported by the Exercise and Sport Science Initiative (ESSI-2018-4), the Urban Collaboratory in the University of Michigan, and the National Science Foundation \u2013 United States (# 1800310).-
dc.language.isoeng-
dc.publisherAmerican Society of Civil Engineers (ASCE)-
dc.subject.meshAdaptive denoising-
dc.subject.meshBuilt environment-
dc.subject.meshDetection accuracy-
dc.subject.meshElectrodermal activity-
dc.subject.meshMeasurement quality-
dc.subject.meshPhotoplethysmogram-
dc.subject.meshQuality improvement-
dc.subject.meshSignal characteristic-
dc.titleReference Signal-Based Method to Remove Respiration Noise in Electrodermal Activity (EDA) Collected from the Field-
dc.typeConference-
dc.citation.conferenceDate2019.6.17. ~ 2019.6.19.-
dc.citation.conferenceNameASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019-
dc.citation.editionComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019-
dc.citation.endPage25-
dc.citation.startPage17-
dc.citation.titleComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019-
dc.identifier.bibliographicCitationComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, pp.17-25-
dc.identifier.scopusid2-s2.0-85078116014-
dc.type.otherConference Paper-
dc.subject.subareaComputer Science (all)-
dc.subject.subareaCivil and Structural Engineering-
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