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View all- Khan IGuerrieri ASerra ESpezzano G(2025)A hybrid deep learning model for UWB radar-based human activity recognitionInternet of Things10.1016/j.iot.2024.10145829(101458)Online publication date: Jan-2025
Radar-based Human Activity Recognition Using Multi-Domain Maps and Feature Fusion Convolutional Neural Network
Authors: 
Wanwan Zhang, Ling HongAuthors Info & Claims
Wanwan Zhang, Ling HongAuthors Info & ClaimsAbstract
Radar-based human activity recognition (HAR) provides a contactless way for a variety of scenarios such as human-computer interaction, smart security, and advanced surveillance with privacy protection. In this paper, we propose a novel HAR method based on fusion map (FuM) and feature fusion convolutional neural network, which is referred to as FuM-MS-Net. Firstly, the time-Doppler map (TDM), time-range map (TRM) and cadence velocity diagram (CVD) are merged into a fusion map. Secondly, a feature fusion convolutional neural network abbreviated as MS-Net is designed, which is composed of two lightweight networks, MobileNetV3-large and ShuffleNetV2. Thirdly, the fusion map is fed into the MS-Net to realize HAR. Finally, the experimental results based on the frequency-modulated continuous-wave (FMCW) radar public dataset from the University of Glasgow show that the proposed method can achieve the recognition accuracy of 96.88%, which prove the effectiveness of the method.
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Published In
March 2024
478 pages
ISBN:9798400716416
DOI:10.1145/3654823
Copyright © 2024 ACM.
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Published: 29 May 2024
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- Refereed limited
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- Natural Science Basic Research Program of Shaanxi Province
- National Natural Science Foundation of China
- Foundational Research Funds for the Central Universities
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CACML 2024
CACML 2024: 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
March 22 - 24, 2024
Shanghai, China
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Overall Acceptance Rate 93 of 241 submissions, 39%
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View all- Khan IGuerrieri ASerra ESpezzano G(2025)A hybrid deep learning model for UWB radar-based human activity recognitionInternet of Things10.1016/j.iot.2024.10145829(101458)Online publication date: Jan-2025
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