Contactless Fall Detection Using mmWave Radar with Stacking-Based Ensemble Learning

Abstract

With the transition to an aging society, increasing attention has been directed toward early detection of falls among elderly individuals. Vision-based fall detection systems suffer from privacy concerns and environmental sensitivity, while wearable sensor-based approaches impose a user burden. To address these limitations, this paper proposes a contactless fall detection method using millimeter-wave (mmWave) radar. Radar data collected in an indoor environment are used to design a stacking-based ensemble learning framework that combines traditional machine learning and deep learning models. The proposed method integrates predictions from multiple base classifiers through a meta-classifier to improve detection performance and robustness. Experimental results using real-world radar data demonstrate that the proposed ensemble model outperforms conventional single classifiers across accuracy, precision, recall, and F1-score. These findings indicate that radar-based sensing, combined with ensemble learning, provides a practical, privacy-preserving solution for indoor fall detection.