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dc.contributor.author
Ma, Yongqiang
dc.contributor.author
Chen, Badong
dc.contributor.author
Ren, Pengju
dc.contributor.author
Zheng, Nanning
dc.contributor.author
Indiveri, Giacomo
dc.contributor.author
Donati, Elisa
dc.date.accessioned
2021-01-14T16:17:41Z
dc.date.available
2020-12-25T06:57:54Z
dc.date.available
2021-01-14T16:17:41Z
dc.date.issued
2020-12
dc.identifier.issn
2156-3357
dc.identifier.other
10.1109/JETCAS.2020.3037951
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/458587
dc.description.abstract
The rapid increase of wearable sensor devices poses new challenges for implementing continuous real-time processing of physiological data. Neuromorphic sensory-processing devices can enable both the measurement of bio-signals and their processing locally in compact embedded wearable systems. In particular, mixed-signal spiking neural networks implemented on neuromorphic processors can be integrated directly with the sensors to extract temporal data-streams in real-time with very low power consumption. In this work, we present a neuromorphic approach for classifying spatio-temporal data from electromyography (EMG) signals, which paves the way toward the realization of compact wearable solutions for neuroprosthetic control. Here we extend previously proposed delta-encoding methods to transform bio-signals into spike trains and use a spiking Recurrent Neural Network (SRNN) architecture to extract features from them. The SRNN was first simulated in software to find the optimal set of hyperparameters, and then validated on the neuromorphic hardware, with a difference in the performance of less than 2%. We describe how biologically plausible mechanisms such as Spike-Timing Dependent Plasticity (STDP) and soft Winner-Take-All (WTA) networks can be exploited to classify the EMG signals and show how their combined use in EMG data classification achieves competitive results with different datasets. Specifically, the classification performance for the Roshambo EMG dataset, which has three different classes, is above 85%, and for the basic finger movements dataset from the Ninapro database, which has eight different classes, reaches 55% accuracy. © 2020 IEEE.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Neuromorphic engineering
en_US
dc.subject
Biomedical signal processing
en_US
dc.subject
Spike encoding
en_US
dc.subject
Spiking recurrent neural network
en_US
dc.subject
Spike-based learning
en_US
dc.subject
Winner-Take-All
en_US
dc.title
EMG-Based Gestures Classification Using a Mixed-Signal Neuromorphic Processing System
en_US
dc.type
Journal Article
dc.date.published
2020-11-16
ethz.journal.title
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
ethz.journal.volume
10
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
IEEE j. emerg. sel. top. circuits syst.
ethz.pages.start
578
en_US
ethz.pages.end
587
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09699 - Indiveri, Giacomo / Indiveri, Giacomo
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09699 - Indiveri, Giacomo / Indiveri, Giacomo
ethz.date.deposited
2020-12-25T06:57:59Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-14T16:17:51Z
ethz.rosetta.lastUpdated
2021-02-15T23:16:04Z
ethz.rosetta.versionExported
true
ethz.COinS
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