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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\u00a0min of data from 20 subjects for this study. We report a classification performance of 98.32\u2009% for SVM and 97.42\u2009% for kNN.<\/jats:p>","DOI":"10.1007\/s11042-021-11105-6","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T12:02:43Z","timestamp":1629460963000},"page":"33527-33546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Fusion of smartphone sensor data for classification of daily user activities"],"prefix":"10.1007","volume":"80","author":[{"given":"G\u00f6khan","family":"\u015eeng\u00fcl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erol","family":"Ozcelik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"11105_CR1","doi-asserted-by":"publisher","unstructured":"Ahmed N, Rafiq JI, Islam MR (2020) Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. 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