Intelligent human-machine interface using hand gestures recognition

Due to the rapid increase of number of industrial or domestic systems that must be controlled it is clear that new, more natural methods of control are needed. This paper presents an intelligent human machine interface based on hand's gesture recognition. The gestures based control system is composed by two subsystems that communicated via radio waves. The first subsystem is a bracelet that captures the movement of the hand using accelerometers. The second subsystem is the control box on which the data processing takes place. Artificial Neural Networks (ANN) are used to add learning capabilities and adaptive behavior to intelligent interfaces that can be used even by elderly or impaired people. Field Programmable Gate Array (FPGA) implementation is an easy an attractive way for hardware implementation. The desired network is modeled, trained and simulated using Neural Network Toolbox. Many networks architecture trained with different methods could be simulated and the network that is best performing for given application is chosen for hardware implementation using System Generator tool developed by Xilinx Inc. This also allows the easy generation of Hardware Description Language (HDL) code from the system representation in Simulink. This HDL design can then be synthesized for implementation in the Xilinx family of FPGA devices.