Abstract:  With the rapidly growing aging population in China， the proportion of the elderly living alone has significantly increased， and thus the aging-population-oriented facilities have received increased attention. In a domestic environment， the elderly are likely to fall down due to different reasons such as lack of care， aging， and sudden illness， which have become one of the main threats to their health. Therefore， monitoring， detecting and predicting fall down behavior of the elderly in real-time can ensure their safety to some extent， while further reducing the life and health risks caused by accidental falling down. Based on a comprehensive overview of the research on human fall detection， we categorize fall detection into two categories： vision-free technologies and computer vision based methods， depending on different kinds of sensors used for data acquisition. We summarize and introduce the system composition of different methods and explore the latest relevant research， and discuss their method characteristics and practical applications. In particular， we focus on reviewing the deep learning based schemes which have been developing rapidly in recent years， while analyzing and discussing relevant principles and research results of deep learning based schemes in details. Next， we also introduce public benchmarking datasets for human fall detection， including dataset size and storage format. Finally， we discuss the prospect for the relevant research， and come up with reasonable suggestions in different aspects.

Key words: fall detection, computer vision, machine learning, deep learning