基于环境智能和移动机器人的跌倒检测系统

L. Ciabattoni, G. Foresi, A. Monteriù, D. P. Pagnotta, L. Tomaiuolo
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引用次数: 10

摘要

本文提出了一种基于深度学习的鲁棒跌倒检测算法和一种配备RGB相机的低成本移动机器人。该方法包括四个步骤。第一步是用户检测,通过实时视频流和深度学习方法实现。一旦检测到用户,则在第二步中估计其位置。在第三步中,如果检测到跌倒,则获取照片并预先注册的音频消息询问用户的情况。在最后一步,拍摄的照片和音频被发送到一个电报机器人(TB),以提醒家庭成员或照顾者。在真实场景中进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fall Detection System by Using Ambient Intelligence and Mobile Robots
In this paper a robust Fall Detection Algorithm by using a deep learning approach and a low-cost mobile robot equipped with an RGB camera is presented. This method consists of four steps. The first step is the user detection, achieved by a real-time video stream and a Deep Learning approach. Once the user is detected, then its position is estimated in the second step. In the third step, if a fall is detected, a photo is acquired and a pre-registered audio message asks the user how he is. In the last step the photo and the audio captured are sent to a Telegram Bot (TB) in order to alert family members or caregivers. Tests have been performed in a real scenario.
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