Volume 8, Issue 2
Hardware Based High Efficient Recognition of 3D Hand Gestures

Yi Liang, Liang Zhuo, Ning Chen, Cheng Cheng, Ruizhi Li & Xinyan Gao

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 337-345.

Published online: 2015-08

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  • 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.

  • Keywords

Gesture Recognition Human Computer Interaction Leap-motion CM1K

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-337, author = {}, 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} }
TY - JOUR T1 - Hardware Based High Efficient Recognition of 3D Hand Gestures JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 337 EP - 345 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00106 UR - https://global-sci.org/intro/article_detail/jfbi/4714.html KW - Gesture Recognition KW - Human Computer Interaction KW - Leap-motion KW - CM1K AB - 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.
Yi Liang, Liang Zhuo, Ning Chen, Cheng Cheng, Ruizhi Li & Xinyan Gao. (2019). Hardware Based High Efficient Recognition of 3D Hand Gestures. Journal of Fiber Bioengineering and Informatics. 8 (2). 337-345. doi:10.3993/jfbim00106
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