We are Alessandra, Luca and Rolando, a group of students from Politecnico di Milano participating at the Xilinx Open Hardware Contest 2016 and developing an open source prosthetic hand, enhanced by FPGA-based control systems.
Myoelectric controlled interfaces have become a major research area in the recent years due to their applications in advanced prostheses. Within this context, the main challenge lies in decoding neural signals in an efficient way in order to command the desired prosthetic actions. Despite many decoding algorithms have been developed, their complexity and computational cost constitute a remarkable limit in real-time applications.
Current myoelectric control methods exploit the EMG signal power in order to control the activation of the prosthetic device. This methodology has revealed to be inaccurate, especially in case of robotic devices involving many Degrees of Freedom (DoF) i.e. prosthetic hands. Furthermore, classification performances are appreciably reduced due to the crosstalk between distinct electrodes and the DOFs are limited by the number of recording channels.
Our project aims at developing SynCH, a 3D-printed prosthetic hand prototype controlled in real-time via Electromyography (EMG). Given the EMG computational cost and limitations, our proposal is to reduce the dimensionality of the signals through an algebraic factorization strategy. Besides, the decoding algorithm employs task-specific muscle synergies to reduce the computational workload undergoing the estimation of muscle activity during different motor tasks. In order to achieve real-time performance, we will leverage the capabilities of the Zynq Embedded Platform to speed-up the feature extraction from the EMG signals.