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http://dx.doi.org/10.12673/jant.2018.22.6.675

Design and Implementation of Hand Gesture Recognizer Based on Artificial Neural Network  

Kim, Minwoo (School of Electronics and Information Engineering, Korea Aerospace University)
Jeong, Woojae (School of Electronics and Information Engineering, Korea Aerospace University)
Cho, Jaechan (School of Electronics and Information Engineering, Korea Aerospace University)
Jung, Yunho (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.22, no.6, 2018 , pp. 675-680 More about this Journal
Abstract
In this paper, we propose a hand gesture recognizer using restricted coulomb energy (RCE) neural network, and present hardware implementation results for real-time learning and recognition. Since RCE-NN has a flexible network architecture and real-time learning process with low complexity, it is suitable for hand recognition applications. The 3D number dataset was created using an FPGA-based test platform and the designed hand gesture recognizer showed 98.8% recognition accuracy for the 3D number dataset. The proposed hand gesture recognizer is implemented in Intel-Altera cyclone IV FPGA and confirmed that it can be implemented with 26,702 logic elements and 258Kbit memory. In addition, real-time learning and recognition verification were performed at an operating frequency of 70MHz.
Keywords
Artificial neural network; Hand gesture recognition; IMU sensor; Machine learning; Restricted coulomb energy;
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