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Gokhan Sengul
Researcher at Atılım University
Publications - 39
Citations - 192
Gokhan Sengul is an academic researcher from Atılım University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 7, co-authored 39 publications receiving 133 citations. Previous affiliations of Gokhan Sengul include Hacettepe University.
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Journal ArticleDOI
Single Camera Photogrammetry System for EEG Electrode Identification and Localization
Ugur Baysal,Gokhan Sengul +1 more
TL;DR: It is found that the proposed system automatically identifies electrodes and localizes them with a maximum error of 0.77 mm and it is suggested that this method may be used in EEG source localization applications in the human brain.
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Deep learning based fall detection using smartwatches for healthcare applications
Rainer Glöckl,Gokhan Sengul,Murat Karakaya,Sanjay Misra,Olusola Abayomi-Alli,Robertas Damasevicius +5 more
TL;DR: In this paper, a smart watch-based system was used to predict fall detection. But the accuracy of the fall detection was only 99.59% and 97.35% when considering only binary classification (falling vs all other activities), perfect accuracy was achieved when considering all activities.
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Fusion of smartphone sensor data for classification of daily user activities
Gokhan Sengul,Erol Ozcelik,Sanjay Misra,Sanjay Misra,Robertas Damasevicius,Rytis Maskeliūnas +5 more
TL;DR: A novel hybrid data fusion method to estimate three types of daily user activities using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone and the k-Nearest Neighbor and Support Vector Machine classifiers.
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Gesture‐based interaction for learning: time to make the dream a reality
Erol Ozcelik,Gokhan Sengul +1 more
TL;DR: Comprehension and, consequently, memory are improved by these acts and gestures facilitate deep and long-lasting learning.
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Gender detection using 3D anthropometric measurements by Kinect
Seda Camalan,Gokhan Sengul,Sanjay Misra,Sanjay Misra,Rytis Maskeliunas,Robertas Damaševičius +5 more
TL;DR: This study has developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection and the maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.