Radar-based hand gesture recognition using spiking neural networks

Ing Jyh Tsang; Federico Corradi; Manolis Sifalakis; Werner Van Leekwijck; Steven Latré

doi:10.3390/electronics10121405

Radar-based hand gesture recognition using spiking neural networks

Ing Jyh Tsang (Corresponding author)
, Federico Corradi
, Manolis Sifalakis
, Werner Van Leekwijck
, Steven Latré

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

42 Citaten (Scopus)

Samenvatting

We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets.

Originele taal-2	Engels
Artikelnummer	1405
Aantal pagina's	20
Tijdschrift	Electronics
Volume	10
Nummer van het tijdschrift	12
DOI's	https://doi.org/10.3390/electronics10121405
Status	Gepubliceerd - 11 jun. 2021
Extern gepubliceerd	Ja

Bibliografische nota

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article.

Financiering

Acknowledgments: The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government.

Financiers
Fonds Wetenschappelijk Onderzoek

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10.3390/electronics10121405

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title = "Radar-based hand gesture recognition using spiking neural networks",

abstract = "We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98\% accuracy on 10-fold cross-validation for both data sets.",

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AU - Tsang, Ing Jyh

AU - Corradi, Federico

AU - Sifalakis, Manolis

AU - Van Leekwijck, Werner

AU - Latré, Steven

PY - 2021/6/11

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AB - We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for different classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets.

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Radar-based hand gesture recognition using spiking neural networks

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