Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
AbstractIn this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works. View Full-Text
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Paraskevopoulos, G.; Spyrou, E.; Sgouropoulos, D.; Giannakopoulos, T.; Mylonas, P. Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data. Algorithms 2019, 12, 108.
Paraskevopoulos G, Spyrou E, Sgouropoulos D, Giannakopoulos T, Mylonas P. Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data. Algorithms. 2019; 12(5):108.Chicago/Turabian Style
Paraskevopoulos, Georgios; Spyrou, Evaggelos; Sgouropoulos, Dimitrios; Giannakopoulos, Theodoros; Mylonas, Phivos. 2019. "Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data." Algorithms 12, no. 5: 108.
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