Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929747703889920 |
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| author | Fard, Ali Samimi Mashhadigholamali, Mohammadreza Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George |
| author_facet | Fard, Ali Samimi Mashhadigholamali, Mohammadreza Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study Fard, Ali Samimi Mashhadigholamali, Mohammadreza Zolfaghari, Samaneh Abedi, Hajar Chakraborty, Mainak Borzì, Luigi Daneshtalab, Masoud Shaker, George Emerging Technologies Artificial Intelligence 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. |
| title | Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study |
| topic | Emerging Technologies Artificial Intelligence |
| url | https://arxiv.org/abs/2503.05629 |