Neuromorphic EMG Processing with Spiking Neural Networks

Projektinformationen

NEPSpiNN

ID Finanzhilfevereinbarung: 753470

DOI

10.3030/753470

Projekt abgeschlossen

EK-Unterschriftsdatum 14 März 2017

Startdatum 1 September 2017

Enddatum 31 August 2019

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 175 419,60

EU-Beitrag

€ 175 419,60

Koordiniert durch

UNIVERSITAT ZURICH
Switzerland

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

Berichtszeitraum: 2017-09-01 bis 2019-08-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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