Spiking Neural Networks for Gesture Recognition Using Time Domain Radar Data
Shaaban A, Furtner W, Weigel R, Lurz F (2022)
Publication Status: Accepted
Publication Type: Conference contribution
Future Publication Type: Conference contribution
Publication year: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 33-36
Conference Proceedings Title: 2022 19th European Radar Conference, EuRAD 2022
Event location: Milan
ISBN: 9782874870712
DOI: 10.23919/EuRAD54643.2022.9924727
Abstract
Gesture recognition using luminance invariant radar sensors is vital due to its extensive use in human-machine interfaces. However, the necessity for computationally expensive radar data pre-processing steps represented by fast Fourier transforms to get range and Doppler features are regarded as a contemporary concern. In this work, we present a solution for gesture recognition that relies on time-domain radar data applied to an event-driven, sparse, and end-to-end trained spiking neural network architecture. Using the proposed solution, it is possible to discriminate between 10 different gestures in a gesture dataset recorded using a 60 GHz frequency-modulated continuous-wave radar sensor, with a mean test accuracy of 93.1%.
Authors with CRIS profile
Ahmed Shaaban
Lehrstuhl für Technische Elektronik
Robert Weigel
Lehrstuhl für Technische Elektronik
Fabian Lurz
Lehrstuhl für Technische Elektronik
Involved external institutions
Germany (DE)How to cite
APA:
Shaaban, A., Furtner, W., Weigel, R., & Lurz, F. (2022). Spiking Neural Networks for Gesture Recognition Using Time Domain Radar Data. In 2022 19th European Radar Conference, EuRAD 2022 (pp. 33-36). Milan: Institute of Electrical and Electronics Engineers Inc..
MLA:
Shaaban, Ahmed, et al. "Spiking Neural Networks for Gesture Recognition Using Time Domain Radar Data." Proceedings of the 19th European Radar Conference, EuRAD 2022, Milan Institute of Electrical and Electronics Engineers Inc., 2022. 33-36.
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