Spiking Neural Networks for Gesture Recognition Using Time Domain Radar Data

被引：0

作者：

Shaaban, Ahmed ^{[1
,2
]}

Furtner, Wolfgang ^{[1
]}

Weigel, Robert ^{[2
]}

Lurz, Fabian ^{[2
]}

机构：

[1] Infineon Technol AG, Munich, Germany

[2] Univ Erlangen Nurnberg, Inst Elect Engn, Erlangen, Germany

来源：

2022 19TH EUROPEAN RADAR CONFERENCE (EURAD) | 2022年

关键词：

Spiking Neural Networks; Radar Gesture Recognition; Convolutional Neural Networks; FMCW Radar; Raw Radar Data; Time Domain Radar Data;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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%.

引用

页码：33 / 36

页数：4

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