Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain.
Sensors (Basel). 2017 Dec 9;17(12):2864. doi: 10.3390/s17122864.
Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.
跌倒 是老年人受伤和死亡的主要原因。不幸的是，跌倒探测器通常基于可穿戴设备，而老年人往往忘记佩戴。此外，基于人工视觉的跌倒探测器尚未在市场上推出。在本文中，我们提出了一种新的基于人工视觉算法的智能家居低成本跌倒探测器。我们的探测器将几种算法（背景减除、卡尔曼滤波和光流）结合在一起，作为具有高精度检测率的机器学习算法的输入。对 50 多个不同跌倒视频的测试表明，检测率大于 96%。
