{"title":"基于信道状态信息的cnn人类行为识别研究","authors":"K. Hwang, Sang-Chul Kim","doi":"10.1109/ICOIN50884.2021.9333879","DOIUrl":null,"url":null,"abstract":"In this paper, we studied a model that can distinguish several different human behaviors. We trained data [1] using the Convolutional Neural Network algorithm. The suggested model showed 94.597% accuracy in distinguishing seven different human activities.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"300 1","pages":"749-751"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Study of CNN-Based Human Behavior Recognition with Channel State Information\",\"authors\":\"K. Hwang, Sang-Chul Kim\",\"doi\":\"10.1109/ICOIN50884.2021.9333879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we studied a model that can distinguish several different human behaviors. We trained data [1] using the Convolutional Neural Network algorithm. The suggested model showed 94.597% accuracy in distinguishing seven different human activities.\",\"PeriodicalId\":6741,\"journal\":{\"name\":\"2021 International Conference on Information Networking (ICOIN)\",\"volume\":\"300 1\",\"pages\":\"749-751\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Information Networking (ICOIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOIN50884.2021.9333879\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Information Networking (ICOIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOIN50884.2021.9333879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Study of CNN-Based Human Behavior Recognition with Channel State Information
In this paper, we studied a model that can distinguish several different human behaviors. We trained data [1] using the Convolutional Neural Network algorithm. The suggested model showed 94.597% accuracy in distinguishing seven different human activities.
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