A robust method for VR-based hand gesture recognition using density-based CNN

Liliana Liliana, Ji-Hun Chae, Joon-Jae Lee, Byung-Gook Lee


Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy.


2D image gesture representation; binary image learning; density-based CNN; hand gesture recognition; VR-based physical treatment;

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DOI: http://dx.doi.org/10.12928/telkomnika.v18i2.14747

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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