Radar-Based Fall Detection: A Survey

Abstract

Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern, where timely detection can greatly minimize harm. With the advancements in radio frequency (RF) technology, radar has emerged as a powerful tool for human fall detection. Traditional machine learning (ML) algorithms, such as support vector machines (SVM) and <italic>k</italic>-nearest neighbors (kNN), have shown promising outcomes. However, deep learning (DL) approaches, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on micro-Doppler, range-Doppler, and range-Doppler-angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal-processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into ML and DL algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of DL in improving radar-based fall detection.