FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care

In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demonstrates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained activities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living.