Abstract:
This thesis explores the possibility of creating a portable, non-invasive, and robust sign language recognition system on a mobile device, capable of improving the quality of life of sign language users. A gesture recognition app was implemented for iOS in Objective-C and C++, using the OpenCV library for computer vision. This app was compared to the Thalmic Labs Myo, which has been successfully used as a sign language recognition platform. Under laboratory conditions, the mobile phone app reached a 96.83% overall classification rate, while the Myo reached a 94.44% classification rate. Despite this accuracy rate for the app, there are severe limitations that make the mobile platform unviable in its current state. However, its promising best-case results suggest that if those limitations can be resolved, then the mobile platform could be as effective as the Myo, and therefore an effective SLR tool.
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/4.0/