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.
@article{arxiv.2503.05629,
title = {Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study},
author = {Ali Samimi Fard and Mohammadreza Mashhadigholamali and Samaneh Zolfaghari and Hajar Abedi and Mainak Chakraborty and Luigi Borzì and Masoud Daneshtalab and George Shaker},
journal= {arXiv preprint arXiv:2503.05629},
year = {2025}
}