Neuromorphic EMG Processing with Spiking Neural Networks

Informacje na temat projektu

NEPSpiNN

Identyfikator umowy o grant: 753470

DOI

10.3030/753470

Projekt został zamknięty

Data podpisania przez KE 14 Marca 2017

Data rozpoczęcia 1 Września 2017

Data zakończenia 31 Sierpnia 2019

Finansowanie w ramach

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Koszt całkowity

€ 175 419,60

Wkład UE

€ 175 419,60

Koordynowany przez

UNIVERSITAT ZURICH
Switzerland

Periodic Reporting for period 1 - NEPSpiNN (Neuromorphic EMG Processing with Spiking Neural Networks)

Okres sprawozdawczy: 2017-09-01 do 2019-08-31

Podsumowanie kontekstu i ogólnych celów projektu

The NEPSpiNN project aimed to realize a neuromorphic event-based neural processing system
that can directly interface with a commercial surface selectromyography (EMG) for hand gesture
classification. The proposed neuromorphic event-based processing classified the hand gestures in
a single step through a full custom hardware implementation of a spiking neural network (SNN)
which extracts spatio-temporal information of EMG signals locally in real-time with very low
power consumption.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

The general objective of the project was to exploit the low-power and low-latency features of a spiking neural network implemented in a mixed analog/digital neuromorphic chip for hand gesture classification.
Main results:
1) develop a spiking neural network able to process EMG signals
2) collection of 3 datasets (open access, zenodo) of EMG from the forearm
3) development and investigation of spiking recurrent neural network on chip
4) mobile application for sensor fusion (EMG+images) for neuroprosthetic control
5) spiking motor controller

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

The project was the first using neuromorphic chip for biomedical applications. It was the first one that was using hardware spiking neural network for EMG classification.
The results of the project have high research and commercial exploitation potentialities. The research area of exploitation was primary neuromorphic engineering and neurorobotics. The project results are a step towards the applications of neuromorphic devices in real-world scenarios, in particular for biomedical applications. This will have a high impact on health and science. The commercial areas of exploitation will be information technology, biomedical applications, prosthetic devices, and real-time computing. During the project development, the researcher already established collaborations with industrial partners that can result in new patents and technology transfer to products that can be introduced in the market.

General project description