Suppr超能文献

智慧城市中面向中老年用户的智能家居系统设计与产品交互体验

The Design of the Lightweight Smart Home System and Interaction Experience of Products for Middle-Aged and Elderly Users in Smart Cities.

机构信息

Department of Art and Design, Shaanxi Fashion Engineering University, Xi'an City 710000, China.

出版信息

Comput Intell Neurosci. 2022 Jun 15;2022:1279351. doi: 10.1155/2022/1279351. eCollection 2022.

Abstract

The research aims to improve the comfort and safety of the smart home by adding a motion recognition algorithm to the smart home system. First, the research status of motion recognition is introduced. Second, based on the requirements of the smart home system, a smart home system is designed for middle-aged and elderly users. The software system in this system includes intelligent control subsystems, intelligent monitoring subsystems, and intelligent protection subsystems. Finally, to increase the security of the smart home, the intelligent monitoring subsystem is improved, and an intelligent security subsystem is proposed based on a small-scale motion detection algorithm. The system uses three three-dimensional (3D) convolutional neural networks (CNNs) to extract three image features, so that the data information in the video can be fully extracted. The performance of the proposed intelligent security subsystem based on a small-scale motion detection algorithm is compared and analyzed. The research results show that the accuracy of the system on the University of Central Florida (UCF101) dataset is 94.64%, and the accuracy on the HMDB51 dataset is 90.11%, which is similar to other advanced algorithms. Observing whether there are dangers such as falling inside and outside the family through motion recognition technology has very important application significance for protecting people's personal safety, life, and health.

摘要

本研究旨在通过向智能家居系统中添加运动识别算法来提高智能家居的舒适性和安全性。首先,介绍运动识别的研究现状。其次,根据智能家居系统的要求,为中老年用户设计智能家居系统。该系统的软件系统包括智能控制子系统、智能监控子系统和智能保护子系统。最后,为了提高智能家居的安全性,改进了智能监控子系统,并提出了一种基于小规模运动检测算法的智能安全子系统。该系统使用三个三维 (3D) 卷积神经网络 (CNN) 提取三个图像特征,从而充分提取视频中的数据信息。对基于小规模运动检测算法的智能安全子系统的性能进行了比较和分析。研究结果表明,该系统在佛罗里达中央大学 (UCF101) 数据集上的准确率为 94.64%,在 HMDB51 数据集上的准确率为 90.11%,与其他先进算法相似。通过运动识别技术观察家庭内外是否存在危险,如跌倒等,对于保护人们的人身安全、生命和健康具有非常重要的应用意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ea8/9217567/cf80df2438a0/CIN2022-1279351.001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验