Sign language static gesture recognition using leap motion

WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
Stephen H. McNeil Jr. (Creator)
Institution
Western Carolina University (WCU )
Web Site: http://library.wcu.edu/
Advisor
Robert Adams

Abstract: Currently scientists and engineers are using near-infrared (NIR) technology to detect motions of human body parts, and can develop programming for gesture recognition. Software is being developed by researchers for both stationary and mobile NIR cameras to operate as Sign Language Recognition devices. This thesis focus was on adapting an application of a NIR camera to be used on any device with application compatibility, essentially allowing a Hearing Impaired (HI) person to use a highly portable camera to communicate with non-speakers of Sign Language. Some examples of the devices that are able to use this technology include computers, tablets, and smart phones. I have developed a Sign Language recognition technology to establish better techniques, databases, and interactive applications for improved function of communication in the HI culture. The algorithm performed at a 76.67% accuracy across all gestures tested for first-time participants.

Additional Information

Publication
Thesis
Language: English
Date: 2017

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