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NEPSpiNN SIGNED

Neuromorphic EMG Processing with Spiking Neural Networks

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

NEPSpiNN project word cloud

Explore the words cloud of the NEPSpiNN project. It provides you a very rough idea of what is the project "NEPSpiNN" about.

remote classification custom realize located extraction invasive signals correlates outputs muscle consumption action decrease skin prostesis chip commercial latency population amount discharged wearable solution spiking processed proposes neural moto interface compact motor ultra behavior neurons activated measured device host stage time deep external dnn difficult data electromyography exoskeleton network integration measuring portable hardware input event computational nepspinn signal muscular semg surface output circuits sequence computers neuromorphic power electrodes electrical understand full transmit potentials classify discrimination amplitude

Project "NEPSpiNN" data sheet

The following table provides information about the project.

Coordinator	UNIVERSITAT ZURICH Organization address address: RAMISTRASSE 71 city: Zürich postcode: 8006 website: http://www.unizh.ch contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Switzerland [CH]
Total cost	175˙419 €
EC max contribution	175˙419 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2016
Funding Scheme	MSCA-IF-EF-ST
Starting year	2017
Duration (year-month-day)	from 2017-09-01 to 2019-08-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	UNIVERSITAT ZURICH UNIVERSITAT ZURICH Organization address address: RAMISTRASSE 71 city: Zürich postcode: 8006 website: http://www.unizh.ch contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	CH (Zürich)	coordinator	175˙419.00

Map

Project objective

The aim of the NEPSpiNN project is to realize a neuromorphic event-based neural processing system that can directly interface with a commercial surface electromyography (sEMG) for the extraction of signal features and classification of the motor neurons output activities. The sEMG is a non-invasive method for measuring the electrical activity, associated to the muscle activities, by means of surface electrodes located above the skin.The amplitude of the sEMG signals, measured in this way, correlates with the number of action potentials discharged by a population of activated motor neurons. To understand the muscular behavior, several measurements are required, which produce a large amount of data, typically, processed by external computers. This makes a wearable solution difficult. In addition, in the current state-of-the-art the sEMG data analyses is composed by three different steps: features extraction, moto neurons outputs discrimination and classification. The steps are activated in sequence increasing the time required for the analyses, that is a problem in real-time applications. The NEPSpiNN project proposes a sEMG analyses stage, implemented in a compact ultra-low power neuromorphic chip, to be able to process data in real-time with low-latency, useful for future implementation of wearable devices. A full custom hardware implementation of a deep neural network (DNN), implemented on neuromorphic spiking neural processing circuits, will classify the motor activities in real time, to find the input for a control system of an external device (e.g. prostesis or exoskeleton). The integration on a unique portable device will allow to decrease the computational cost of processing and the power consumption. This enables a system that can be integrated in a wearable solution without the necessity to transmit data to a remote host.

Publications

List of publications.
year	authors and title	journal	last update
2019	Enea Ceolini, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati Sensor fusion using EMG and vision for hand gesture classification in mobile applications published pages: , ISSN: , DOI:		2019-11-07
2018	Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Krause, Karla Burelo, Giacomo Indiveri, Thomas Dalgaty, Elisa Vianello Processing EMG signals using reservoir computing on an event-based neuromorphic system published pages: , ISSN: , DOI:		2019-11-07
2018	Elisa Donati, Fernando Perez-PeÃ±a, Chiara Bartolozzi, Giacomo Indiveri, Elisabetta Chicca Open-loop neuromorphic controller implemented on vlsi devices published pages: , ISSN: , DOI:		2019-11-07
2019	Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Barbara Krause, Giacomo Indiveri Discrimination of EMG Signals Using a Neuromorphic Implementation of a Spiking Neural Network published pages: , ISSN: 1932-4545, DOI:	IEEE transactions on biomedical circuits and systems	2019-11-07

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The information about "NEPSPINN" are provided by the European Opendata Portal: CORDIS opendata.