Nowadays, detecting aberrant health issues is a difficult process. Falling, especially among the elderly, is a severe concern worldwide. Falls can result in deadly consequences, including unconsciousness, internal bleeding, and often times, death. A practical and optimal, smart approach of detecting falling is currently a concern. The use of vision-based fall monitoring is becoming more common among scientists as it enables senior citizens and those with other health conditions to live independently. For tracking, surveillance, and rescue, unmanned aerial vehicles use video or image segmentation and object detection methods. The Tello drone is equipped with a camera and with this device we determined normal and abnormal behaviors among our participants. The autonomous falling objects are classified using a convolutional neural network (CNN) classifier. The results demonstrate that the systems can identify falling objects with a precision of 0.9948.

翻译：如今，检测异常健康问题是一个困难的过程。跌倒，尤其是在老年人中，是全球范围内的严重问题。跌倒可能导致致命后果，包括意识丧失、内出血，甚至死亡。一种实用且最优化的智能跌倒检测方法目前备受关注。基于视觉的跌倒监测在科学家中越来越普遍，因为它使老年人及其他健康状况不佳者能够独立生活。在跟踪、监控和救援方面，无人机使用视频或图像分割与目标检测方法。Tello无人机配备摄像头，通过此设备我们确定了参与者的正常与异常行为。自主跌倒物体通过卷积神经网络（CNN）分类器进行分类。结果表明，该系统能够以0.9948的精度识别跌倒物体。