Advances in Software Engineering
Volume 2013 (2013), Article ID 707248, 13 pages
Research Article

Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

1Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
2Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
3Department of Engineering, Ming Chi University of Technology, Taipei 243, Taiwan

Received 30 July 2012; Revised 27 December 2012; Accepted 17 January 2013

Academic Editor: Christine W. Chan

Copyright © 2013 Priyanka Mekala et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.