Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module

The increasing integration of technology in our daily lives demands the development of more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operati...

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Autores principales:	Mateusz Chmurski, Gianfranco Mauro, Avik Santra, Mariusz Zubert, Gökberk Dagasan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	edge computing Edge TPU optimization quantization FMCW radar Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f11070adb7264dbda7fdd7902e00ba6e
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spelling	oai:doaj.org-article:f11070adb7264dbda7fdd7902e00ba6e2021-11-11T19:15:03ZHighly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module10.3390/s212172981424-8220https://doaj.org/article/f11070adb7264dbda7fdd7902e00ba6e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7298https://doaj.org/toc/1424-8220The increasing integration of technology in our daily lives demands the development of more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.Mateusz ChmurskiGianfranco MauroAvik SantraMariusz ZubertGökberk DagasanMDPI AGarticleedge computingEdge TPUoptimizationquantizationFMCWradarChemical technologyTP1-1185ENSensors, Vol 21, Iss 7298, p 7298 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	edge computing Edge TPU optimization quantization FMCW radar Chemical technology TP1-1185
spellingShingle	edge computing Edge TPU optimization quantization FMCW radar Chemical technology TP1-1185 Mateusz Chmurski Gianfranco Mauro Avik Santra Mariusz Zubert Gökberk Dagasan Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
description	The increasing integration of technology in our daily lives demands the development of more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.
format	article
author	Mateusz Chmurski Gianfranco Mauro Avik Santra Mariusz Zubert Gökberk Dagasan
author_facet	Mateusz Chmurski Gianfranco Mauro Avik Santra Mariusz Zubert Gökberk Dagasan
author_sort	Mateusz Chmurski
title	Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
title_short	Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
title_full	Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
title_fullStr	Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
title_full_unstemmed	Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
title_sort	highly-optimized radar-based gesture recognition system with depthwise expansion module
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f11070adb7264dbda7fdd7902e00ba6e
work_keys_str_mv	AT mateuszchmurski highlyoptimizedradarbasedgesturerecognitionsystemwithdepthwiseexpansionmodule AT gianfrancomauro highlyoptimizedradarbasedgesturerecognitionsystemwithdepthwiseexpansionmodule AT aviksantra highlyoptimizedradarbasedgesturerecognitionsystemwithdepthwiseexpansionmodule AT mariuszzubert highlyoptimizedradarbasedgesturerecognitionsystemwithdepthwiseexpansionmodule AT gokberkdagasan highlyoptimizedradarbasedgesturerecognitionsystemwithdepthwiseexpansionmodule
_version_	1718431620532273152

Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module

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