Recent Advances in the Application of Motion Capture Technology in Fall Prevention for the Elderly: A Systematic Review (Preprint)
Fang Wen, Weifeng Zhang, Xinzheng Wang · 2025
BACKGROUND With the global aging population, falls among the elderly have become a major public health challenge, significantly impacting the safety and well-being of older adults. Fall prevention systems based on motion capture technology have emerged as a research hotspot in the field of medical health. This technology enables real-time monitoring and analysis of elderly individuals' movement states, allowing for the identification of fall risks and the provision of timely interventions. OBJECTIVE This review aims to summarize the latest advancements of the application of motion capture technology in fall prevention for the elderly. METHODS We conducted a systematic search in the Web of Science database. Key search terms included “Motion capture” and “elderly,” “old people,” “older adults,” as well as the keyword “fall prevention.” The search scope covered research articles published between January 2014 and March 2025, focusing on studies utilizing motion capture technology for fall detection, prevention, rehabilitation, intervention training, gait monitoring, and abnormal activity monitoring. RESULTS A total of 88 studies met the inclusion criteria. We identified and categorized five main research themes and four main motion capture technologies. The five research directions include: fall detection and prevention systems, gait analysis and stability assessment, rehabilitation assistive technologies and devices, daily activity monitoring and behavior recognition, and virtual reality and gamified rehabilitation. The main motion capture technologies include: optical motion capture, inertial motion capture, computer vision-based markerless motion capture, and other motion capture technologies. The “other motion capture” category includes audio signals, radar signals, Wi-Fi signals, and multimodal fusion technologies. Commonly used motion capture devices include inertial measurement units (IMUs), infrared cameras, standard RGB cameras, or depth sensors. The most commonly used data classification and processing algorithm is the Convolutional Neural Network (CNN). Most studies focus on elderly individuals around 60 years of age residing in nursing homes, care facilities, hospitals, and single-occupancy residences. CONCLUSIONS With the continuous development of emerging technologies such as artificial intelligence, motion capture technology in medical rehabilitation and sports science will become more widespread and in-depth, providing more precise, scientific, and efficient motion capture methods. Multimodal motion capture technology will become more accurate and comprehensive, serving as a reliable solution for capturing complex motion scenarios. Future research can focus on the integration of emerging technologies such as artificial intelligence, which will optimize motion capture algorithms and models, thereby improving the efficiency and accuracy of movement performance analysis. The primary research objective is to enhance the quality of life for the elderly and create a favorable well-being environment for them.