The elderly population is increasing rapidly around the world. There are no enough caretakers for them. Use of AI-based in-home medical care systems is gaining momentum due to this. Human fall detection is one of the most important tasks of medical care system for the aged people. Human fall is a common problem among elderly people. Detection of a fall and providing medical help as early as possible is very important to reduce any further complexity. The chances of death and other medical complications can be reduced by detecting and providing medical help as early as possible after the fall. There are many state-of-the-art fall detection techniques available these days, but the majority of them need very high computing power. In this paper, we proposed a lightweight and fast human fall detection system using pose estimation. We used `Movenet' for human joins key-points extraction. Our proposed method can work in real-time on any low-computing device with any basic camera. All computation can be processed locally, so there is no problem of privacy of the subject. We used two datasets `GMDCSA' and `URFD' for the experiment. We got the sensitivity value of 0.9375 and 0.9167 for the dataset `GMDCSA' and `URFD' respectively. The source code and the dataset GMDCSA of our work are available online to access.

翻译：全球老年人口正在快速增长，而与之配套的照护人员严重不足。基于人工智能的家庭医疗监护系统因此日益受到关注。人体跌倒检测是老年医疗监护系统最重要的任务之一。跌倒是老年人常见问题，及时检测跌倒并尽早提供医疗救助对降低并发症风险至关重要。通过跌倒后快速检测并实施医疗干预，可显著降低死亡风险及其他医学并发症发生率。现有多种先进的跌倒检测技术，但大多数需要极高的计算能力。本文提出一种基于姿态估计的轻量级快速人体跌倒检测系统。我们采用`Movenet`进行人体关键点提取，该方法可在任何配备普通摄像头的低算力设备上实现实时运行。所有计算均在本地完成，有效保护受试者隐私。实验采用`GMDCSA`和`URFD`两个数据集，在`GMDCSA`数据集上获得0.9375的灵敏度值，在`URFD`数据集上获得0.9167的灵敏度值。本研究的源代码及GMDCSA数据集已在线公开。