Toward an embedded system for gesture recognition based on artificial neural network using reconfigurable target - case study and review


  • Reda Ahmad Miskolci Egyetem
  • Alshoufi Tareq Miskolci Egyetem
  • Bouzid Ahmed Miskolci Egyetem
  • Vásárhelyi József Miskolci Egyetem



Gesture Recognition, HW/SW co-design, Data Acquisition, Motor Control, IMU


With a view to create an intelligent remote control for robot movements, this article treats the study case of dataset creation using RSG (Reference Signal Generator). Using artificial intelligence, the device recognizes the gestures of an operator. Indeed, a neural network can classify time series data coming from accelerometers, and for a beginning 4 gestures are taken into consideration. The most challenging work is to build a reference dataset that is necessary for the learning process. To train the neural network, a huge amount of reference data should be created (hundreds of thousands of time-series vectors per gesture per sensor), which cannot be done manually by an operator. To overcome the issue, an RSG is created. This article also describes how a 1-DoF arm has been designed to emulate the behavior of the human arm doing gestures as well as the data acquisition system. The system is based on a software/hardware co-design implemented on Programmable System on Chip (PSoC).