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.

翻译：跌倒检测，尤其是对老年人等高危人群至关重要，是一项关键的公共卫生问题，及时检测能极大减少伤害。随着射频技术的进步，雷达已成为强大的人体检测与追踪工具。支持向量机和K近邻等传统机器学习算法已展现出良好效果。然而，深度学习算法，特别是卷积神经网络和循环神经网络，在学习复杂特征和处理大型非结构化数据方面表现更优。本文对基于雷达的跌倒检测进行了深入综述，重点探讨了微多普勒、距离-多普勒以及距离-多普勒-角度技术。我们讨论了跌倒检测中的复杂性和挑战，并强调了在多种影响因素下，明确跌倒定义及制定适当检测标准的必要性。我们概述了雷达信号处理原理及基于雷达的跌倒检测的基础技术，为理解机器学习和深度学习算法提供了简明视角。在分析2000年以来发表的74篇基于雷达的跌倒检测研究论文后，我们旨在弥合当前研究空白，并强调未来潜在的研究策略，着重探讨了现实应用的可能性以及深度学习在提升基于雷达的跌倒检测方面的未开发潜力。