超宽带数据作为高效网络和LSTM架构的输入,用于人类活动识别

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Ambient Intelligence and Smart Environments Pub Date : 2022-05-10 DOI:10.3233/ais-210462
Alexandre Beaulieu, Florentin Thullier, K. Bouchard, Julien Maître, S. Gaboury
{"title":"超宽带数据作为高效网络和LSTM架构的输入,用于人类活动识别","authors":"Alexandre Beaulieu, Florentin Thullier, K. Bouchard, Julien Maître, S. Gaboury","doi":"10.3233/ais-210462","DOIUrl":null,"url":null,"abstract":"The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"1 1","pages":"157-172"},"PeriodicalIF":1.8000,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition\",\"authors\":\"Alexandre Beaulieu, Florentin Thullier, K. Bouchard, Julien Maître, S. Gaboury\",\"doi\":\"10.3233/ais-210462\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements.\",\"PeriodicalId\":49316,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Smart Environments\",\"volume\":\"1 1\",\"pages\":\"157-172\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2022-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Smart Environments\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/ais-210462\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Smart Environments","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/ais-210462","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 2

摘要

在过去的几年里,世界人口正在老龄化,这一趋势预计将在未来增加。在日常生活中需要援助的人数预计也会增加。幸运的是,智能家居正成为人类直接监督的一个越来越有吸引力的选择。智能家居配备了传感器,再加上人工智能(AI),可以在需要时为居住者提供支持。问题的核心是对活动的认识。人类活动识别是一个复杂的问题,因为可用的传感器种类繁多,它们对隐私的影响很大,可能的活动数量很多,甚至一个简单的活动也有几种方法可以执行。本文提出了一种深度学习模型,结合LSTM和effentnet模型的优化版本,使用迁移学习、数据融合、极简预处理以及使用来自三个超宽带(UWB)雷达的数据进行活动和运动识别训练。在活动识别方面,实验是在一个真实的、有家具的公寓里进行的,10名参与者进行了15种不同的活动。结果表明,使用Leave-One-Subject-Out交叉验证,在相同数据集上的前一精度提高了65.59%,提高了18.63%。此外,针对运动识别的实验是在相同的条件下进行的,要求单个参与者在三个超宽带雷达位于两个不同高度的情况下进行四种不同的手臂运动。Top-1的总体准确率为73%,对所得结果的详细分析表明,所提出的模型能够准确识别大粒度和细粒度的运动。然而,由于四种运动之间的差异程度不足,中等大小的运动表现出对运动识别的显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Ultra-wideband data as input of a combined EfficientNet and LSTM architecture for human activity recognition
The world population is aging in the last few years and this trend is expected to increase in the future. The number of persons requiring assistance in their everyday life is also expected to rise. Luckily, smart homes are becoming a more and more compelling alternative to direct human supervision. Smart homes are equipped with sensors that, coupled with Artificial Intelligence (AI), can support their occupants whenever needed. At the heart of the problem is the recognition of activities. Human activity recognition is a complex problem due to the variety of sensors available, their impact on privacy, the high number of possible activities, and the several ways even a simple activity can be performed. This paper proposes a deep learning model combining LSTM and a tuned version of the EfficientNet model using transfer learning, data fusion, minimalist pre-processing as well as training for both activity and movement recognition using data from three ultra-wideband (UWB) radars. As regards activity recognition, experiments were conducted in a real and furnished apartment where 15 different activities were performed by 10 participants. Results showed an improvement of 18.63% over previous work on the same dataset with 65.59% in Top-1 accuracy using Leave-One-Subject-Out cross validation. Furthermore, the experiments that address movement recognition were conducted under the same conditions where a single participant was asked to perform four distinct arm movements with the three UWB radars positioned at two different heights. With an overall accuracy of 73% in Top-1, the detailed analysis of the results obtained showed that the proposed model was capable of recognizing accurately large and fine-grained movements. However, the medium-sized movements demonstrated a significant impact on the movement recognition due to an insufficient degree of variation between the four proposed movements.
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
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.30
自引率
17.60%
发文量
23
审稿时长
>12 weeks
期刊介绍: The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.
期刊最新文献
Evaluation factors of adopting smart home IoT: The hybrid fuzzy MCDM approach for robot vacuum Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment From programming-to-modeling-to-prompts smart ubiquitous applications A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications Memoization based priority-aware task management for QoS provisioning in IoT gateways
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1