Shiming Chen, Chunjing Xiao, Yanhui Han, Xianghe Du
{"title":"A Real-time Activity Recognition System based on Dynamic Adaptive Windows using WiFi Signals","authors":"Shiming Chen, Chunjing Xiao, Yanhui Han, Xianghe Du","doi":"10.1145/3507548.3507566","DOIUrl":null,"url":null,"abstract":"WiFi Chanel State Information (CSI)-based activity recognition has attracted much attention in recent years. And it is extremely vital to recognize activities in time, especially for dangerous activities such as fall. In this paper, we present a real-time activity recognition system. In this system, we design a dynamic threshold-based activity segmentation method, which can address the problems of the fixed threshold and single window, and accurately detect start and end points of activities. The experiments demonstrate that our system acquires expected recognition performance.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3507548.3507566","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
WiFi Chanel State Information (CSI)-based activity recognition has attracted much attention in recent years. And it is extremely vital to recognize activities in time, especially for dangerous activities such as fall. In this paper, we present a real-time activity recognition system. In this system, we design a dynamic threshold-based activity segmentation method, which can address the problems of the fixed threshold and single window, and accurately detect start and end points of activities. The experiments demonstrate that our system acquires expected recognition performance.
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