当前位置: X-MOL 学术J. Ambient Intell. Smart Environ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Real time fall detection using infrared cameras and reflective tapes under day/night luminance
Journal of Ambient Intelligence and Smart Environments ( IF 1.8 ) Pub Date : 2021-06-18 , DOI: 10.3233/ais-210605
E. Ramanujam 1 , S. Padmavathi 1
Affiliation  

Falls are the leading cause of injuries and death in elderly individuals who live alone at home. The core service of assistive living technology is to monitor elders’ activities through wearable devices, ambient sensors, and vision systems. Vision systems are among the best solutions, as their implementation and maintenance costs are the lowest. However, current vision systems are limited in their ability to handle cluttered environments, occlusion, illumination changes throughout the day, and monitoring without illumination. To overcome these issues, this paper proposes a 24/7 monitoring system for elders that uses retroreflective tape fabricated as part of conventional clothing, monitored through low-cost infrared (IR) cameras fixed in the living environment. IR camera records video even when there are changes in illumination or zero luminance. For classification among clutter and occlusion, the tape is considered as a blob instead of a human silhouette; the orientation angle, fitted through ellipse modeling, of the blob in each frame allows classification that detects falls without pretrained data. System performance was tested using subjects in various age groups and “fall” or “non-fall” were detected with 99.01% accuracy.

中文翻译:

在白天/夜晚亮度下使用红外摄像机和反光带进行实时跌倒检测

跌倒是独居老人受伤和死亡的主要原因。辅助生活技术的核心服务是通过可穿戴设备、环境传感器和视觉系统监控老年人的活动。视觉系统是最佳解决方案之一,因为它们的实施和维护成本最低。然而,当前的视觉系统在处理杂乱环境、遮挡、全天光照变化以及无光照监控的能力方面受到限制。为了克服这些问题,本文提出了一种 24/7 全天候老年人监控系统,该系统使用作为传统服装一部分制作的反光带,通过固定在生活环境中的低成本红外 (IR) 摄像机进行监控。即使在照明或零亮度发生变化时,红外摄像机也能记录视频。对于杂波和遮挡之间的分类,胶带被视为斑点而不是人体轮廓;通过椭圆建模拟合的每个帧中斑点的方向角允许分类,无需预训练数据即可检测跌倒。系统性能使用不同年龄组的受试者进行测试,检测到“跌倒”或“未跌倒”的准确率为 99.01%。
更新日期:2021-06-18
down
wechat
bug