Real-time monitoring of elderly people through computer vision

IF 0.8 Q4 ROBOTICS
Abhijeet Ravankar, Arpit Rawankar, Ankit A. Ravankar
{"title":"Real-time monitoring of elderly people through computer vision","authors":"Abhijeet Ravankar,&nbsp;Arpit Rawankar,&nbsp;Ankit A. Ravankar","doi":"10.1007/s10015-023-00882-y","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, many countries including Japan are facing the problems of increasing old-age population and shortage of labor. This has increased the demands of automating several tasks using robots and artificial intelligence in agriculture, production, and healthcare sectors. With increasing old-age population, an increasing number of people are expected to be admitted in old-age home and rehabilitation centers in the coming years where they receive proper care and attention. In such a scenario, it can be foreseen that it will be increasingly difficult to accurately monitor each patient. This requires an automation of patient’s activity detection. To this end, this paper proposes to use computer vision for automatic detection of patient’s behavior. The proposed work first detects the pose of the patient through a Convolution Neural Network. Next, the coordinates of the different body parts are detected. These coordinates are input in the decision generation layer which uses the relationship between the coordinates to predict the person’s actions. This paper focuses on the detection of important activities like: sudden fall, sitting, eating, sleeping, exercise, and computer usage. Although previous works in behavior detection focused only on detecting a particular activity, the proposed work can detect multiple activities in real-time. We verify the proposed system thorough experiments in real environment with actual sensors. The experimental results shows that the proposed system can accurately detect the activities of the patient in the room. Critical scenarios like sudden fall are detected and an alarm is raised for immediate support. Moreover, the the privacy of the patient is preserved though an ID based method in which only the detected activities are chronologically stored in the database.</p></div>","PeriodicalId":46050,"journal":{"name":"Artificial Life and Robotics","volume":"28 3","pages":"496 - 501"},"PeriodicalIF":0.8000,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10015-023-00882-y.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Life and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s10015-023-00882-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

In recent years, many countries including Japan are facing the problems of increasing old-age population and shortage of labor. This has increased the demands of automating several tasks using robots and artificial intelligence in agriculture, production, and healthcare sectors. With increasing old-age population, an increasing number of people are expected to be admitted in old-age home and rehabilitation centers in the coming years where they receive proper care and attention. In such a scenario, it can be foreseen that it will be increasingly difficult to accurately monitor each patient. This requires an automation of patient’s activity detection. To this end, this paper proposes to use computer vision for automatic detection of patient’s behavior. The proposed work first detects the pose of the patient through a Convolution Neural Network. Next, the coordinates of the different body parts are detected. These coordinates are input in the decision generation layer which uses the relationship between the coordinates to predict the person’s actions. This paper focuses on the detection of important activities like: sudden fall, sitting, eating, sleeping, exercise, and computer usage. Although previous works in behavior detection focused only on detecting a particular activity, the proposed work can detect multiple activities in real-time. We verify the proposed system thorough experiments in real environment with actual sensors. The experimental results shows that the proposed system can accurately detect the activities of the patient in the room. Critical scenarios like sudden fall are detected and an alarm is raised for immediate support. Moreover, the the privacy of the patient is preserved though an ID based method in which only the detected activities are chronologically stored in the database.

Abstract Image

计算机视觉对老年人的实时监测
近年来,包括日本在内的许多国家都面临着老龄人口增加和劳动力短缺的问题。这增加了农业、生产和医疗保健部门使用机器人和人工智能自动化多项任务的需求。随着老年人口的增加,预计未来几年将有越来越多的人入住养老院和康复中心,在那里他们将得到适当的照顾和照顾。在这种情况下,可以预见,准确监测每位患者将变得越来越困难。这需要患者活动检测的自动化。为此,本文提出利用计算机视觉对患者的行为进行自动检测。提出的工作首先通过卷积神经网络检测患者的姿势。接下来,检测不同身体部位的坐标。这些坐标被输入到决策生成层中,决策生成层使用坐标之间的关系来预测人的动作。本文重点检测重要活动,如:突然跌倒、坐着、吃饭、睡觉、锻炼和使用电脑。尽管以前在行为检测方面的工作只关注于检测特定的活动,但所提出的工作可以实时检测多个活动。我们用实际传感器在真实环境中进行了深入的实验,验证了所提出的系统。实验结果表明,该系统能够准确地检测出患者在房间内的活动。检测到突然坠落等关键情况,并发出警报以获得即时支持。此外,通过基于ID的方法,仅将检测到的活动按时间顺序存储在数据库中,从而保护了患者的隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信