Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study

• URL: http://arxiv.org/abs/2503.05629v1
• Date: Fri, 07 Mar 2025 17:53:29 GMT
• Title: Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study
• Authors: Ali Samimi Fard, Mohammadreza Mashhadigholamali, Samaneh Zolfaghari, Hajar Abedi, Mainak Chakraborty, Luigi Borzì, Masoud Daneshtalab, George Shaker,
• Abstract summary: This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition.<n>Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps as data vectors.<n>The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51%.
• Score: 2.251010251400407
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human Activity Recognition has gained significant attention due to its diverse applications, including ambient assisted living and remote sensing. Wearable sensor-based solutions often suffer from user discomfort and reliability issues, while video-based methods raise privacy concerns and perform poorly in low-light conditions or long ranges. This study introduces a Frequency-Modulated Continuous Wave radar-based framework for human activity recognition, leveraging a 60 GHz radar and multi-dimensional feature maps. Unlike conventional approaches that process feature maps as images, this study feeds multi-dimensional feature maps -- Range-Doppler, Range-Azimuth, and Range-Elevation -- as data vectors directly into the machine learning (SVM, MLP) and deep learning (CNN, LSTM, ConvLSTM) models, preserving the spatial and temporal structures of the data. These features were extracted from a novel dataset with seven activity classes and validated using two different validation approaches. The ConvLSTM model outperformed conventional machine learning and deep learning models, achieving an accuracy of 90.51% and an F1-score of 87.31% on cross-scene validation and an accuracy of 89.56% and an F1-score of 87.15% on leave-one-person-out cross-validation. The results highlight the approach's potential for scalable, non-intrusive, and privacy-preserving activity monitoring in real-world scenarios.

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