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Title: A real-time ASL recognition system using Leap Motion sensors
Authors: Fok, KY
Ganganath, N
Cheng, CT 
Tse, CK 
Keywords: Sign language recognition
Sensor fusion
Depth sensors
Hidden Markov models
Leap Motion sensor
American sign language
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Xi'an, China, 17-19 Sept. 2015, p. 411 - 414 How to cite?
Abstract: It is always challenging for deaf and speech-impaired people to communicate with non-sign language users. A real-time sign language recognition system using 3D motion sensors could lower the aforementioned communication barrier. However, most existing gesture recognition systems are adopting a single sensor framework, whose performance is susceptible to occlusions. In this paper, we proposed a real-time multi-sensor recognition system for American sign language (ASL). Data collected from Leap Motion sensors are fused using multiple sensors data fusion (MSDF) and the recognition is performed using hidden Markov models (HMM). Experimental results demonstrate that the proposed system can deliver higher recognition accuracy over single-sensor systems. Due to its low implementation cost and higher accuracy, the proposed system can be widely deployed and bring conveniences to sign language users.
ISBN: 978-1-4673-9199-3
DOI: 10.1109/CyberC.2015.81
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Fok, K. -., Ganganath, N., Cheng, C. -., & Tse, C. K. (2015). A real-time ASL recognition system using leap motion sensors. Paper presented at the Proceedings - 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2015, 411-414 is available at 10.1109/CyberC.2015.81
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