Şengül, GökhanSengul, GokhanOzcelik, ErolÖzçelik, ErolMisra, SanjayMısra, SanjayDamasevicius, RobertasMaskeliunas, RytisComputer Engineering2024-07-052024-07-052021151380-75011573-772110.1007/s11042-021-11105-62-s2.0-85113190488https://doi.org/10.1007/s11042-021-11105-6https://hdl.handle.net/20.500.14411/2028Misra, Sanjay/0000-0002-3556-9331; Damaševičius, Robertas/0000-0001-9990-1084; Maskeliunas, Rytis/0000-0002-2809-2213; Şengül, Gökhan/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 fusionArticleQ2Q280243352733546WOS:000686840500002