{"title":"一种上下文感知的时空架构","authors":"S. Thiemjarus, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BSN.2006.5","DOIUrl":null,"url":null,"abstract":"Context-aware sensing is an integral part of the body sensor network (BSN) design and it allows the understanding of intrinsic characteristics of the sensed signal and determination of how BSNs should react to different events and adapt its monitoring behaviour. The purpose of this paper is to propose a novel spatio-temporal self-organising map that minimises the number of neurons involved whilst maintaining a high accuracy in class separation for both static and dynamic activities","PeriodicalId":246227,"journal":{"name":"International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"A spatio-temporal architecture for context aware sensing\",\"authors\":\"S. Thiemjarus, Benny P. L. Lo, Guang-Zhong Yang\",\"doi\":\"10.1109/BSN.2006.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Context-aware sensing is an integral part of the body sensor network (BSN) design and it allows the understanding of intrinsic characteristics of the sensed signal and determination of how BSNs should react to different events and adapt its monitoring behaviour. The purpose of this paper is to propose a novel spatio-temporal self-organising map that minimises the number of neurons involved whilst maintaining a high accuracy in class separation for both static and dynamic activities\",\"PeriodicalId\":246227,\"journal\":{\"name\":\"International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BSN.2006.5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2006.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A spatio-temporal architecture for context aware sensing
Context-aware sensing is an integral part of the body sensor network (BSN) design and it allows the understanding of intrinsic characteristics of the sensed signal and determination of how BSNs should react to different events and adapt its monitoring behaviour. The purpose of this paper is to propose a novel spatio-temporal self-organising map that minimises the number of neurons involved whilst maintaining a high accuracy in class separation for both static and dynamic activities
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