Resource Efficient Gesture Sensing Based on FMCW Radar using Spiking Neural Networks

Arsalan M, Chmurski M, Santra A, El-Masry M, Weigel R, Issakov V (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

IEEE MTT-S International Microwave Symposium Digest Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2021-June

Pages Range: 78-81

Conference Proceedings Title: IEEE MTT-S International Microwave Symposium Digest

Event location: Virtual, Atlanta, GA, USA

ISBN: 9781665403078

DOI: 10.1109/IMS19712.2021.9574994

Abstract

Gesture recognition is a natural and intuitive human computer-interface compared to traditional interfaces such as mouse and keyboards. Radar forms a promising modality for portable gesture recognition systems, where minute finger motions can be easily sensed and processed to extract meaningful information presenting the user's intention. Compared to several conventional deep learning approaches that have been proposed in the literature, in this paper we present a novel spiking neural network (SNNs) for gesture recognition using a 60-GHz frequency modulated continuous wave radar (FMCW) chipset. SNNs are more hardware friendly and energy-efficient than their deep learning counterparts making them attractive for portable devices. We have demonstrated in measurement the application of SNN for the processing of radar data.

Authors with CRIS profile

Robert Weigel Lehrstuhl für Technische Elektronik (LTE)

Involved external institutions

Otto-von-Guericke-Universität Magdeburg DE Germany (DE) Infineon Technologies AG DE Germany (DE)

How to cite

APA:

Arsalan, M., Chmurski, M., Santra, A., El-Masry, M., Weigel, R., & Issakov, V. (2021). Resource Efficient Gesture Sensing Based on FMCW Radar using Spiking Neural Networks. In IEEE MTT-S International Microwave Symposium Digest (pp. 78-81). Virtual, Atlanta, GA, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Arsalan, Muhammad, et al. "Resource Efficient Gesture Sensing Based on FMCW Radar using Spiking Neural Networks." Proceedings of the 2021 IEEE MTT-S International Microwave Symposium, IMS 2021, Virtual, Atlanta, GA, USA Institute of Electrical and Electronics Engineers Inc., 2021. 78-81.

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