  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 technology, radar has
emerged as a powerful tool for human detection and tracking. Traditional
machine learning algorithms, such as Support Vector Machines (SVM) and
k-Nearest Neighbors (kNN), have shown promising outcomes. However, deep
learning approaches, notably Convolutional Neural Networks (CNN) and Recurrent
Neural Networks (RNN), 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
machine learning and deep learning 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 deep learning in improving radar-based fall detection.
