老年人活动识别与居住监测的计算机视觉方法

Q3 Medicine
Sudhir Gaikwad , Shripad Bhatlawande , Swati Shilaskar , Anjali Solanke
{"title":"老年人活动识别与居住监测的计算机视觉方法","authors":"Sudhir Gaikwad ,&nbsp;Shripad Bhatlawande ,&nbsp;Swati Shilaskar ,&nbsp;Anjali Solanke","doi":"10.1016/j.medntd.2023.100272","DOIUrl":null,"url":null,"abstract":"<div><p>In this study, we explore a human activity recognition (HAR) system using computer vision for assisted living systems (ALS). Most existing HAR systems are implemented using wired or wireless sensor networks. These systems have limitations such as cost, power issues, weight, and the inability of the elderly to wear and carry them comfortably. These issues could be overcome by a computer vision based HAR system. But such systems require a highly memory-consuming image dataset. Training such a dataset takes a long time. The proposed computer-vision-based system overcomes the shortcomings of existing systems. The authors have used key-joint angles, distances between the key joints, and slopes between the key joints to create a numerical dataset instead of an image dataset. All these parameters in the dataset are recorded via real-time event simulation. The data set has 780,000 calculated feature values from 20,000 images. This dataset is used to train and detect five different human postures. These are sitting, standing, walking, lying, and falling. The implementation encompasses four distinct algorithms: the decision tree (DT), random forest (RF), support vector machine (SVM), and an ensemble approach. Remarkably, the ensemble technique exhibited exceptional performance metrics with 99 ​% accuracy, 98 ​% precision, 97 ​% recall, and an F1 score of 99 ​%.</p></div>","PeriodicalId":33783,"journal":{"name":"Medicine in Novel Technology and Devices","volume":"20 ","pages":"Article 100272"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259009352300067X/pdfft?md5=d53618fe6ac4153f8e4bb0c416a9bf0b&pid=1-s2.0-S259009352300067X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A computer vision-approach for activity recognition and residential monitoring of elderly people\",\"authors\":\"Sudhir Gaikwad ,&nbsp;Shripad Bhatlawande ,&nbsp;Swati Shilaskar ,&nbsp;Anjali Solanke\",\"doi\":\"10.1016/j.medntd.2023.100272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this study, we explore a human activity recognition (HAR) system using computer vision for assisted living systems (ALS). Most existing HAR systems are implemented using wired or wireless sensor networks. These systems have limitations such as cost, power issues, weight, and the inability of the elderly to wear and carry them comfortably. These issues could be overcome by a computer vision based HAR system. But such systems require a highly memory-consuming image dataset. Training such a dataset takes a long time. The proposed computer-vision-based system overcomes the shortcomings of existing systems. The authors have used key-joint angles, distances between the key joints, and slopes between the key joints to create a numerical dataset instead of an image dataset. All these parameters in the dataset are recorded via real-time event simulation. The data set has 780,000 calculated feature values from 20,000 images. This dataset is used to train and detect five different human postures. These are sitting, standing, walking, lying, and falling. The implementation encompasses four distinct algorithms: the decision tree (DT), random forest (RF), support vector machine (SVM), and an ensemble approach. Remarkably, the ensemble technique exhibited exceptional performance metrics with 99 ​% accuracy, 98 ​% precision, 97 ​% recall, and an F1 score of 99 ​%.</p></div>\",\"PeriodicalId\":33783,\"journal\":{\"name\":\"Medicine in Novel Technology and Devices\",\"volume\":\"20 \",\"pages\":\"Article 100272\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S259009352300067X/pdfft?md5=d53618fe6ac4153f8e4bb0c416a9bf0b&pid=1-s2.0-S259009352300067X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medicine in Novel Technology and Devices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S259009352300067X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicine in Novel Technology and Devices","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S259009352300067X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

在这项研究中,我们探索了一种使用计算机视觉辅助生活系统(ALS)的人类活动识别(HAR)系统。大多数现有的HAR系统都是使用有线或无线传感器网络实现的。这些系统有一些限制,比如成本、电力问题、重量,以及老年人无法舒适地佩戴和携带它们。这些问题可以通过基于计算机视觉的HAR系统来克服。但是这样的系统需要高度消耗内存的图像数据集。训练这样的数据集需要很长时间。提出的基于计算机视觉的系统克服了现有系统的不足。作者使用键连接角度、键连接之间的距离和键连接之间的斜率来创建数值数据集,而不是图像数据集。数据集中的所有参数都是通过实时事件模拟记录的。该数据集从20,000张图像中计算出780,000个特征值。该数据集用于训练和检测五种不同的人体姿势。这些是坐着、站着、走着、躺着和跌倒。该实现包含四种不同的算法:决策树(DT)、随机森林(RF)、支持向量机(SVM)和集成方法。值得注意的是,集成技术表现出优异的性能指标,准确率为99%,精确度为98%,召回率为97%,F1分数为99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A computer vision-approach for activity recognition and residential monitoring of elderly people

In this study, we explore a human activity recognition (HAR) system using computer vision for assisted living systems (ALS). Most existing HAR systems are implemented using wired or wireless sensor networks. These systems have limitations such as cost, power issues, weight, and the inability of the elderly to wear and carry them comfortably. These issues could be overcome by a computer vision based HAR system. But such systems require a highly memory-consuming image dataset. Training such a dataset takes a long time. The proposed computer-vision-based system overcomes the shortcomings of existing systems. The authors have used key-joint angles, distances between the key joints, and slopes between the key joints to create a numerical dataset instead of an image dataset. All these parameters in the dataset are recorded via real-time event simulation. The data set has 780,000 calculated feature values from 20,000 images. This dataset is used to train and detect five different human postures. These are sitting, standing, walking, lying, and falling. The implementation encompasses four distinct algorithms: the decision tree (DT), random forest (RF), support vector machine (SVM), and an ensemble approach. Remarkably, the ensemble technique exhibited exceptional performance metrics with 99 ​% accuracy, 98 ​% precision, 97 ​% recall, and an F1 score of 99 ​%.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Medicine in Novel Technology and Devices
Medicine in Novel Technology and Devices Medicine-Medicine (miscellaneous)
CiteScore
3.00
自引率
0.00%
发文量
74
审稿时长
64 days
×
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