Stress detection on smartphone data with a machine learning approach based on Mahalanobis distance-based outlier finding and ReliefF feature selection

Basit Öğe Kaydı
dc.authoridBALLI, Serkan/0000-0002-4825-139X
dc.authorwosidBALLI, Serkan/D-4751-2018
dc.contributor.authorSagbas, Ensar Arif
dc.contributor.authorKorukoglu, Serdar
dc.contributor.authorBalli, Serkan
dc.date.accessioned2023-01-12T20:18:54Z
dc.date.available2023-01-12T20:18:54Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractStress is beneficial when a person is focused, awake and alert. However, exposure to high doses of stress harms a person's health. For this reason, it is important to detect stress and begin relief as soon as possible. In this study, soft keyboard typing behaviors with touchscreen panel, gravity, linear acceleration, and gyroscope data obtained from smartphones were examined. It was observed that there was a correlation between the results obtained and typing behaviors and the stress levels of individuals. In this context, an expanded data set was created. In order to detect stress with higher accuracy, a Mahalanobis distance-based outlier detection approach was applied. Subsequently, a structure was created by combining the ReliefF feature selection method and machine learning techniques to identify efficient features and perform classification. The results obtained by cleaning outlier data showed that the created structures achieved success with high accuracy. In addition, outlier detection and cleaning increased the classification success by 1.77 points.en_US
dc.identifier.doi10.5505/pajes.2021.88724
dc.identifier.endpage345en_US
dc.identifier.issn1300-7009
dc.identifier.issn2147-5881
dc.identifier.issn1300-7009en_US
dc.identifier.issn2147-5881en_US
dc.identifier.issue2en_US
dc.identifier.startpage333en_US
dc.identifier.urihttps://doi.org/10.5505/pajes.2021.88724
dc.identifier.urihttps://hdl.handle.net/11454/78966
dc.identifier.volume28en_US
dc.identifier.wosWOS:000819870500015en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherPamukkale Univen_US
dc.relation.ispartofPamukkale University Journal Of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStress detectionen_US
dc.subjectMahalanobis distanceen_US
dc.subjectFeature evaluationen_US
dc.subjectSmartphone dataen_US
dc.subjectTyping behavioren_US
dc.subjectRecognitionen_US
dc.subjectSensorsen_US
dc.titleStress detection on smartphone data with a machine learning approach based on Mahalanobis distance-based outlier finding and ReliefF feature selectionen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu