Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures
Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures
复制标题
基于 3D 人体关键姿势的老年人日常生活活动 (ADL) 监测
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
M. Thonnat
中科院分区：
文献类型：
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作者：
N. Zouba;Bernard Boulay;F. Brémond;M. Thonnat
本文提出了一种认知视觉的方法来识别一组有趣的日常生活活动（ADL）的老人在家里。所提出的方法是由一个视频分析组件和一个活动识别组件。
视频分析部分包括人物检测、人物跟踪和人体姿态识别。人体姿态识别由一组姿态模型和一个专用的人体姿态识别算法组成。
活动识别部分包含一组视频事件模型和一个专用的视频事件识别算法。
在这项研究中，我们与医学专家（来自尼斯医院的老年病学家）合作，定义和建模一组与老年人有趣的活动相关的场景。这些活动中的一些需要检测人体的精细描述，例如姿势。为此，我们提出了10个3D关键的人体姿势有用的识别一组有趣的人类活动，无论环境。使用这些3D关键的人体姿势，我们已经建模了34个视频事件，简单的如“一个人站着”和复合的如“一个人感觉晕倒”。我们还采用了视频事件识别算法，通过添加姿势来检测真实的感兴趣的活动。
我们的方法的新奇是建议的3D关键姿势和一套活动模型的老人独自生活在她/他自己的家。
为了验证我们提出的模型，我们在Gerhome实验室进行了一系列实验，该实验室是一个真实的网站，再现了典型公寓的环境。对于这些实验，我们已经获得并处理了十个视频序列与一个演员。每个视频序列的持续时间约为10分钟，每个视频包含约4800帧。
This paper presents a cognitive vision approach to recognize a set of interesting activities of daily living (ADLs) for elderly at home. The proposed approach is composed of a video analysis component and an activity recognition component.
A video analysis component contains person detection, person tracking and human posture recognition. A human posture recognition is composed of a set of postures models and a dedicated human posture recognition algorithm.
Activity recognition component contains a set of video event models and a dedicated video event recognition algorithm.
In this study, we collaborate with medical experts (gerontologists from Nice hospital) to define and model a set of scenarios related to the interesting activities of elderly. Some of these activities require to detect a fine description of human body such as postures. For this purpose, we propose ten 3D key human postures usefull to recognize a set of interesting human activities regardless of the environment. Using these 3D key human postures, we have modeled thirty four video events, simple ones such as "a person is standing" and composite ones such as "a person is feeling faint". We have also adapted a video event recognition algorithm to detect in real time some activities of interest by adding posture.
The novelty of our approach is the proposed 3D key postures and the set of activity models of elderly person living alone in her/his own home.
To validate our proposed models, we have performed a set of experiments in the Gerhome laboratory which is a realistic site reproducing the environment of a typical apartment. For these experiments, we have acquired and processed ten video sequences with one actor. The duration of each video sequence is about ten minutes and each video contains about 4800 frames.