WiCAR:用于 WiFi 活动识别的类递增系统

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhihua Li , Shuli Ning , Bin Lian , Chao Wang , Zhongcheng Wei
{"title":"WiCAR:用于 WiFi 活动识别的类递增系统","authors":"Zhihua Li ,&nbsp;Shuli Ning ,&nbsp;Bin Lian ,&nbsp;Chao Wang ,&nbsp;Zhongcheng Wei","doi":"10.1016/j.pmcj.2024.101963","DOIUrl":null,"url":null,"abstract":"<div><p>The proposal of Integrated Sensing and Communications has once again drawn researchers’ attention to WiFi sensing, propelling applications based on WiFi sensing into an advanced stage. However, the current field of activity recognition only identifies fixed categories of activities, neglecting the growing demand for perceiving activity types in real applications over time. In response to the issue, we present WiCAR, a WiFi activity recognition system designed for class incremental scenarios. WiCAR takes antenna array-fused image data as input, employing the Wi-RA model with parallel stacked activation functions as its backbone network. To alleviate the typical catastrophic forgetting issue in class-incremental learning, WiCAR employs a strategy of replaying known data. Additionally, we adopts knowledge distillation to improve accuracy among old samples during the incremental process. To tackle the imbalance in the number of samples between old and new classes, the model is updated through weight alignment. This serious of strategies endows the system with the capability to progressively learn and handle new classes. We conducted extensive experiments to evaluate the system performance. The experimental results demonstrate that our system exhibits excellent performance regardless of the number of tasks, whether tasks are uniform or non-uniform, and the order of task arrivals. The highest average accuracy reaches 96.429%, and even in the presence of six incremental stages, the average accuracy remains at 92.867%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WiCAR: A class-incremental system for WiFi activity recognition\",\"authors\":\"Zhihua Li ,&nbsp;Shuli Ning ,&nbsp;Bin Lian ,&nbsp;Chao Wang ,&nbsp;Zhongcheng Wei\",\"doi\":\"10.1016/j.pmcj.2024.101963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The proposal of Integrated Sensing and Communications has once again drawn researchers’ attention to WiFi sensing, propelling applications based on WiFi sensing into an advanced stage. However, the current field of activity recognition only identifies fixed categories of activities, neglecting the growing demand for perceiving activity types in real applications over time. In response to the issue, we present WiCAR, a WiFi activity recognition system designed for class incremental scenarios. WiCAR takes antenna array-fused image data as input, employing the Wi-RA model with parallel stacked activation functions as its backbone network. To alleviate the typical catastrophic forgetting issue in class-incremental learning, WiCAR employs a strategy of replaying known data. Additionally, we adopts knowledge distillation to improve accuracy among old samples during the incremental process. To tackle the imbalance in the number of samples between old and new classes, the model is updated through weight alignment. This serious of strategies endows the system with the capability to progressively learn and handle new classes. We conducted extensive experiments to evaluate the system performance. The experimental results demonstrate that our system exhibits excellent performance regardless of the number of tasks, whether tasks are uniform or non-uniform, and the order of task arrivals. The highest average accuracy reaches 96.429%, and even in the presence of six incremental stages, the average accuracy remains at 92.867%.</p></div>\",\"PeriodicalId\":49005,\"journal\":{\"name\":\"Pervasive and Mobile Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pervasive and Mobile Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574119224000889\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pervasive and Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574119224000889","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

综合传感与通信 "的提出再次吸引了研究人员对 WiFi 传感的关注,推动基于 WiFi 传感的应用进入高级阶段。然而,目前的活动识别领域只能识别固定类别的活动,而忽视了在实际应用中对随时间变化的活动类型的感知这一日益增长的需求。针对这一问题,我们提出了 WiCAR,一个专为类别递增场景设计的 WiFi 活动识别系统。WiCAR 以天线阵列融合图像数据为输入,采用具有并行堆叠激活函数的 Wi-RA 模型作为骨干网络。为了缓解类递增学习中典型的灾难性遗忘问题,WiCAR 采用了重放已知数据的策略。此外,我们还采用了知识蒸馏技术,以提高增量过程中旧样本的准确性。为了解决新旧类之间样本数量不平衡的问题,我们通过权重对齐来更新模型。这一系列策略赋予了系统逐步学习和处理新类别的能力。我们进行了大量实验来评估系统性能。实验结果表明,无论任务数量多少、任务是均匀的还是非均匀的、任务到达的顺序如何,我们的系统都表现出了卓越的性能。最高平均准确率达到 96.429%,即使在有六个增量阶段的情况下,平均准确率也保持在 92.867%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WiCAR: A class-incremental system for WiFi activity recognition

The proposal of Integrated Sensing and Communications has once again drawn researchers’ attention to WiFi sensing, propelling applications based on WiFi sensing into an advanced stage. However, the current field of activity recognition only identifies fixed categories of activities, neglecting the growing demand for perceiving activity types in real applications over time. In response to the issue, we present WiCAR, a WiFi activity recognition system designed for class incremental scenarios. WiCAR takes antenna array-fused image data as input, employing the Wi-RA model with parallel stacked activation functions as its backbone network. To alleviate the typical catastrophic forgetting issue in class-incremental learning, WiCAR employs a strategy of replaying known data. Additionally, we adopts knowledge distillation to improve accuracy among old samples during the incremental process. To tackle the imbalance in the number of samples between old and new classes, the model is updated through weight alignment. This serious of strategies endows the system with the capability to progressively learn and handle new classes. We conducted extensive experiments to evaluate the system performance. The experimental results demonstrate that our system exhibits excellent performance regardless of the number of tasks, whether tasks are uniform or non-uniform, and the order of task arrivals. The highest average accuracy reaches 96.429%, and even in the presence of six incremental stages, the average accuracy remains at 92.867%.

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
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
×
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