Evaluation of Spiking Neural Networks for Time Domain-based Radar Hand Gesture Recognition

Shaaban A, Furtner W, Weigel R, Lurz F (2022)

Publication Type: Conference contribution

Publication year: 2022

Publisher: IEEE Computer Society

Book Volume: 2022-September

Pages Range: 474-479

Conference Proceedings Title: Proceedings International Radar Symposium

Event location: Gdansk PL

ISBN: 9788395602054

Abstract

Radar-based hand gesture recognition is a promising alternative to the camera-based solutions since radar is not impacted by lighting conditions and has no privacy concerns. Energy consumption is a key concern for radar applications on edge devices. Thus, a time-domain-based training approach that avoids the computationally expensive pre-processing fast Fourier transform (FFT) steps and utilizes time-domain radar data has been used. Spiking neural networks (SNNs) are recognized as being lower-power and more energy-efficient than artificial neural networks (ANNs). Therefore, we used the time-domain training approach alongside SNNs to conserve the most energy. This work evaluates several convolutional-based SNNs and their ANN variants to determine the SNNs appropriateness for temporally based datasets and their ability to learn complex spatio-temporal features. All models were trained using only time-domain data and then used to classify ten different gestures recorded by five different people using a 60 GHz frequency-modulated continuous-wave (FMCW) radar sensor. The results indicate the effectiveness of the used time-domain training approach and the ability of SNNs to outperform their ANN counterparts.

Authors with CRIS profile

Robert Weigel Lehrstuhl für Technische Elektronik (LTE) Fabian Lurz Lehrstuhl für Technische Elektronik (LTE)

Involved external institutions

Infineon Technologies AG DE Germany (DE)

How to cite

APA:

Shaaban, A., Furtner, W., Weigel, R., & Lurz, F. (2022). Evaluation of Spiking Neural Networks for Time Domain-based Radar Hand Gesture Recognition. In Proceedings International Radar Symposium (pp. 474-479). Gdansk, PL: IEEE Computer Society.

MLA:

Shaaban, Ahmed, et al. "Evaluation of Spiking Neural Networks for Time Domain-based Radar Hand Gesture Recognition." Proceedings of the 23rd International Radar Symposium, IRS 2022, Gdansk IEEE Computer Society, 2022. 474-479.

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