@Article{JFBI-8-337,
author = {Yi Liang, Liang Zhuo, Ning Chen, Cheng Cheng, Ruizhi Li and Xinyan Gao},
title = {Hardware Based High Efficient Recognition of 3D Hand Gestures},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2015},
volume = {8},
number = {2},
pages = {337--345},
abstract = {This paper addresses the technical issues related to hand gestures generation and their real-time
recognition. The arbitrary 3D gestures are generated by the Leap-motion Controller which detects
and tracks the hands and fingers to acquire position and motion information. We combine Leap-motion
sensor and hardware based pattern engine to make gesture recognition easier and propose an efficient
recognition solution involving a neuron chip (named CM1K). In our experiment, we used one-finger
gesture cases to demonstrate the efficiency of our solution. Experimental results showed that our solution
owns a high accuracy in gesture data acquiring and only costs a few milliseconds in recognizing speed.
We also considered the situation of similar gestures recognition and analyzed the causes of low matching
rate from specific data.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00106},
url = {http://global-sci.org/intro/article_detail/jfbi/4714.html}
}