A Gait Fall Detection System for the Elderly Based on Wearable Devices and Machine Learning Algorithms

作者：W. J. Xu, Yutian Chen, Qianyi Zhang, Chengmeng Zhang, Chen Gong, Yang Xiao · 年份：2025 · DOI：10.1109/isas66241.2025.11101923 · 被引用次数：2 · 研究领域：Context-Aware Activity Recognition Systems、Gait Recognition and Analysis、IoT and GPS-based Vehicle Safety Systems

The older people are often at high risk of falling because of various factors like the age-related issues which affect their control over muscles or because of weakness due to age. There are other factors like the neurological disorders, side effects of medicines that may also lead to a fall in elderly people. This can be very risky for older people as it may lead to serious injuries or sometimes death. So, the detection of fall and preventing it by using technology is important. The technology has enabled us to use wearable gadgets that can help in monitoring the falls in the elderly population by using Machine Learning algorithms. This paper discusses different technologies and wearable gadgets that together make a fall detection and prevention system for the aged people. The challenges, trends and future aspects of wearable devices and the integration of machine learning for preventing fall has also been discussed in this paper.