Neuromorphic Edge Computing for Biomedical Applications: Gesture Classification Using EMG Signals
dc.contributor.author
Vitale, Antonio
dc.contributor.author
Donati, Elisa
dc.contributor.author
Germann, Roger
dc.contributor.author
Magno, Michele
dc.date.accessioned
2022-10-31T12:49:48Z
dc.date.available
2022-10-30T03:08:46Z
dc.date.available
2022-10-31T12:49:48Z
dc.date.issued
2022-10
dc.identifier.issn
1530-437X
dc.identifier.issn
1558-1748
dc.identifier.other
10.1109/JSEN.2022.3194678
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/578454
dc.description.abstract
With the emergence of edge-computing platforms, the applications of smart wearable devices are immense. This technology can be incorporated in consumer products such as smartwatches and activity trackers, for continuous health monitoring, as well as for medical applications such as myoelectric prosthetics, to interpret the electric activity in the residual limb and achieve fast and precise control. However, wearable technologies require a lightweight, energy-efficient, and low-latency processing system in order to extend the devices' autonomy while maintaining a realistic user-feedback interaction. Neuromorphic processing, thanks to its event-based and asynchronous nature, presents ideal characteristics for compact brain-inspired low-power and ultra-fast computing systems that can enable a new generation of wearable devices. This article presents two spiking neural networks (SNNs) for event-based electromyography (EMG) gesture recognition and their evaluation on Intel's research neuromorphic chip Loihi. Specifically, the evaluation is done on the Kapoho Bay platform which embeds the Loihi processor in a Universal Serial Bus (USB) form factor device allowing for closed-loop edge computation. With accurate experimental evaluation, this article demonstrates that the proposed method is able to discriminate 12 different hand gestures using an eight-channel EMG sensor and exceeds state-of-the-art results. We obtained an accuracy of 74% on the commonly used NinaPro DB5 dataset, a processing latency of 5.7 ms for 300-ms EMG samples while consuming only 41 mW.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Electromyography
en_US
dc.subject
Sensors
en_US
dc.subject
Neuromorphics
en_US
dc.subject
Feature extraction
en_US
dc.subject
Prosthetics
en_US
dc.subject
Convolutional neural networks
en_US
dc.subject
Biomedical monitoring
en_US
dc.subject
Deep learning
en_US
dc.subject
edge-computing
en_US
dc.subject
electromyography (EMG) signal processing
en_US
dc.subject
event-based programming
en_US
dc.subject
neuromorphic hardware
en_US
dc.subject
spiking neural networks (SNNs)
en_US
dc.title
Neuromorphic Edge Computing for Biomedical Applications: Gesture Classification Using EMG Signals
en_US
dc.type
Journal Article
dc.date.published
2022-08-03
ethz.journal.title
IEEE Sensors Journal
ethz.journal.volume
22
en_US
ethz.journal.issue
20
en_US
ethz.journal.abbreviated
IEEE Sens. J.
ethz.pages.start
19490
en_US
ethz.pages.end
19499
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
2022-10-30T03:08:53Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-10-31T12:49:49Z
ethz.rosetta.lastUpdated
2024-02-02T18:50:24Z
ethz.rosetta.versionExported
true
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