School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Department of Electrical Engineering (ESAT), Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium.
Sensors (Basel). 2021 Jul 29;21(15):5134. doi: 10.3390/s21155134.
Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.
跌倒属于不常见动作，但会为老年人带来严重的健康风险。老龄人口比例不断增加，因此迫切需要开发跌倒检测和预防系统。新兴技术专注于开发此类系统，以提高生活质量，尤其是针对老年人。跌倒预防系统旨在预测和降低跌倒风险。相比之下，跌倒检测系统则用于观察跌倒并生成帮助通知，以将跌倒的后果降至最低。现有大量专注于跌倒检测的技术和综述论文。同样，一些研究相对较为陈旧，仅关注可穿戴设备，且使用基于统计和阈值的方法，导致误报率较高。因此，本文使用机器学习 (ML) 算法介绍了跌倒检测和预防系统的最新研究趋势。它使用了最近的研究并分析了数据集、年龄组、ML 算法、传感器和位置。此外，它还详细讨论了跌倒检测和预防系统的当前趋势，并提出了可能的未来方向。该概述可以帮助研究人员了解当前系统，并通过改进突出问题来提出新的方法。
