Fusion of Smartphone Sensor Data for Classification of Daily User Activities

dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorid Damaševičius, Robertas/0000-0001-9990-1084
dc.authorid Maskeliunas, Rytis/0000-0002-2809-2213
dc.authorid Şengül, Gökhan/0000-0003-2273-4411
dc.authorscopusid 8402817900
dc.authorscopusid 26424777100
dc.authorscopusid 56962766700
dc.authorscopusid 6603451290
dc.authorscopusid 27467587600
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.authorwosid Damaševičius, Robertas/E-1387-2017
dc.authorwosid Sengul, Gokhan/G-8213-2016
dc.authorwosid Maskeliunas, Rytis/J-7173-2017
dc.authorwosid Şengül, Gökhan/AAA-2788-2022
dc.contributor.author Sengul, Gokhan
dc.contributor.author Ozcelik, Erol
dc.contributor.author Misra, Sanjay
dc.contributor.author Damasevicius, Robertas
dc.contributor.author Maskeliunas, Rytis
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:19:52Z
dc.date.available 2024-07-05T15:19:52Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp [Sengul, Gokhan; Misra, Sanjay] Atilim Univ, Dept Comp Engn, AnkaraKizilcasar Mah, Incek, Turkey; [Ozcelik, Erol] Cankaya Univ, Yukariyurtcu Mahallesi,Mimar Sinan Caddesi 4, TR-06790 Ankara, Turkey; [Misra, Sanjay] Covenant Univ, Dept Elect & Informat Engn, Ota 0123, Nigeria; [Damasevicius, Robertas] Silesian Tech Univ, Fac Appl Math, Kaszubska 23, PL-44100 Gliwice, Poland; [Maskeliunas, Rytis] Vytautas Magnus Univ, Dept Appl Informat, Vileikos 8, Kaunas, Lithuania en_US
dc.description Misra, Sanjay/0000-0002-3556-9331; Damaševičius, Robertas/0000-0001-9990-1084; Maskeliunas, Rytis/0000-0002-2809-2213; Şengül, Gökhan/0000-0003-2273-4411 en_US
dc.description.abstract New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN. en_US
dc.identifier.citationcount 15
dc.identifier.doi 10.1007/s11042-021-11105-6
dc.identifier.endpage 33546 en_US
dc.identifier.issn 1380-7501
dc.identifier.issn 1573-7721
dc.identifier.issue 24 en_US
dc.identifier.scopus 2-s2.0-85113190488
dc.identifier.scopusquality Q2
dc.identifier.startpage 33527 en_US
dc.identifier.uri https://doi.org/10.1007/s11042-021-11105-6
dc.identifier.uri https://hdl.handle.net/20.500.14411/2028
dc.identifier.volume 80 en_US
dc.identifier.wos WOS:000686840500002
dc.identifier.wosquality Q2
dc.institutionauthor Şengül, Gökhan
dc.institutionauthor Özçelik, Erol
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 20
dc.subject Human activity recognition en_US
dc.subject Wearable intelligence en_US
dc.subject Feature fusion en_US
dc.title Fusion of Smartphone Sensor Data for Classification of Daily User Activities en_US
dc.type Article en_US
dc.wos.citedbyCount 16
dspace.entity.type Publication
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