Clark, Andrew and Moodley, Deshendran (2016) A System for a Hand Gesture-Manipulated Virtual Reality Environment, Proceedings of Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT 2016), 26-28 September 2016, Johannesburg, South Africa, ACM New York, USA.
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Abstract
Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.
| Item Type: | Conference paper |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence Computing methodologies > Computer graphics |
| Alternate Locations: | http://dl.acm.org/citation.cfm?id=2987511 |
| Date Deposited: | 18 Nov 2016 |
| Last Modified: | 10 Oct 2019 15:32 |
| URI: | http://pubs.cs.uct.ac.za/id/eprint/1113 |
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