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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning
Song, SeungeonKim, BongseokKim, SangdongLee, Jonghun
DGIST Authors
Song, SeungeonKim, BongseokKim, SangdongLee, Jonghun
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Author Keywords
AlexNetCNNDeep learningDoppler radarFoot gestureGesture recognitionSTFTSVD
AutomationBaseballDoppler radarGesture recognitionHands-freeHigh compressionsLearning modelsMemory efficiencyNew highRadar signatureSmart homesSurrounding environmentDeep learningImage compressionLearning systemsRadar imagingSingular value decomposition
Recently, Doppler radar‐based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar‐based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high‐compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high‐compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Multidisciplinary Digital Publishing Institute (MDPI)
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Appears in Collections:
Division of Automotive Technology Advanced Radar Tech. Lab 1. Journal Articles
Division of Automotive Technology 1. Journal Articles


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