Ozcelik, ErolMisra, SanjayDamasevicius, RobertasMaskeliunas, RytisSengul, Gokhan02.04. Psikoloji02. Fen-Edebiyat Fakültesi01. Çankaya Üniversitesi2022-05-112025-09-182022-05-112025-09-182021Şengül, Gökhan...at all (2021). "Fusion of smartphone sensor data for classification of daily user activities", Multimedia Tools and Applications, Vol. 80, No. 24, pp. 33527-33546.1380-75011573-7721https://doi.org/10.1007/s11042-021-11105-6https://hdl.handle.net/20.500.12416/14382Misra, Sanjay/0000-0002-3556-9331; Maskeliunas, Rytis/0000-0002-2809-2213; Sengul, Gokhan/0000-0003-2273-4411New 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.eninfo:eu-repo/semantics/openAccessHuman Activity RecognitionWearable IntelligenceFeature FusionArticle10.1007/s11042-021-11105-62-s2.0-85113190488