Neural network based algorithm for hand gesture detection in a low-cost microprocessor applications

In this paper the simple architecture of neural network for hand gesture classification was presented. The network classifies the previously calculated parameters of EMG signals. The main goal of this project was to develop simple solution that is not computationally complex and can be implemented on microprocessors in low-cost 3D printed prosthetic arms. As the part of conducted research the data set EMG signals corresponding to 5 different gestures was created. The accuracy of elaborated solution was 90% when applied real time on data sampled with 1kHz frequency and 75% when applied real time on data acquired and process directly on microprocessor with lower, 100Hz sampling frequency.