Analysis of Non-Contact Fall Detection Technologies - Huang, Yanqi (Transactions on Computer Science and Intelligent Systems Research, 2025)
Against the backdrop of an aging society, the health of middle-aged and elderly individuals has become a growing concern for families. Falls are the leading cause of injury-related deaths, with a significant number of older adults suffering from disabilities or fatalities due to falls each year. Therefore, detecting falls is critically important. Traditional wearable sensors face challenges such as algorithmic limitations and wearability issues, making it difficult to meet the increasing demands of the elderly. Thus, research on non-contact fall detection technologies is essential. To explore various non-contact fall detection methods, this paper examines four types of technologies: those based on WiFi, computer vision, radar, and the multimodal fusion of WiFi and RGB. The paper is divided into three parts: the first introduces the theoretical foundations of each technology, briefly explaining their mechanisms and highlights; the second compares the advantages, disadvantages, and applicable experimental scenarios of each technology; and the third summarizes current technological limitations and offers prospects for future development. By analyzing the principles and characteristics of various technologies, this study presents suitable application scenarios and comparative strengths and weaknesses, providing a theoretical reference and practical guidance for future research on non-contact fall detection, thereby facilitating the optimization and implementation of these technologies.
Keywords: Fall Detection; Radar; YOLOv8; CSI.