Fall detection systems are crucial for identifying falls and ensuring timely assistance, thus reducing the risk of serious injuries. With the development of society and increasing attention to health issues, researchers have conducted extensive studies on falls to reduce the severe sequelae of falls. Integrating fall detection systems with the Internet of Things (IoT), particularly the Internet of Medical Things (IoMT), has significantly advanced healthcare and personal safety. This dynamic relationship between fall detection technology and IoT has opened up new vistas for monitoring and assisting individuals, particularly the elderly and those with health conditions that make them prone to falls. This paper presents a review of wearable sensor-based fall detection techniques. We classify the detection methods into their categories from an algorithmic perspective: threshold-based, conventional machine learning-based, and deep learning-based methods. In addition, we identify and summarize the available datasets that can be used to evaluate the performance of the introduced methods. This review aims to provide researchers with a better comprehension of the fall detection problem, intending to foster further advancements in the field.

中文翻译：

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基于可穿戴传感器的医疗物联网跌倒检测系统：综述


跌倒检测系统对于识别跌倒并确保及时提供援助至关重要，从而降低严重伤害的风险。随着社会的发展和人们对健康问题的日益重视，研究人员对跌倒进行了广泛的研究，以减少跌倒造成的严重后遗症。将跌倒检测系统与物联网 (IoT)，特别是医疗物联网 (IoMT) 集成，可以显着提高医疗保健和个人安全。跌倒检测技术和物联网之间的这种动态关系为监测和帮助个人开辟了新的前景，特别是老年人和那些容易跌倒的健康状况的人。本文综述了基于可穿戴传感器的跌倒检测技术。我们从算法的角度将检测方法分为几类：基于阈值的方法、基于传统机器学习的方法和基于深度学习的方法。此外，我们还确定并总结了可用于评估所引入方法的性能的可用数据集。本综述旨在让研究人员更好地理解跌倒检测问题，旨在促进该领域的进一步进步。