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Article Citation - WoS: 47Citation - Scopus: 66Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications(Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, RobertasWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation - WoS: 71Citation - Scopus: 87Efficient Route Planning for an Unmanned Air Vehicle Deployed on a Moving Carrier(Springer, 2016) Savuran, Halil; Karakaya, MuratVehicle routing problem (VRP) is a constrained extension of the well-known traveling salesman problem (TSP). Emerging from the current conceptual trends in operations field, a new constraint to be included to the existing VRP parameters is the depot mobility. A practical example of such a problem is planning a route for an Unmanned air vehicle (UAV) deployed on a mobile platform to visit fixed targets. Furthermore, the range constraint of the UAV becomes another constraint within this sample case as well. In this paper, we define new VRP variants by introducing depot mobility (Mobile Depot VRP: MoDVRP) and extending it with capacity constraint (Capacitated MoDVRP: C-MoDVRP). As a sample use case, we study route planning for a UAV deployed on a moving carrier. To deal with the C-MoDVRP, we propose a Genetic Algorithm that is adapted to satisfy the constraints of depot mobility and range, while maximizing the number of targets visited by the UAV. To examine the success of our approach, we compare the individual performances of our proposed genetic operators with conventional ones and the performance of our overall solution with the Nearest Neighbor and Hill Climbing heuristics, on some well-known TSP benchmark problems, and receive successful results.
