Real-time Human Activity Recognition using WiFi Channel State Information

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/93285

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93285

Title:	Real-time Human Activity Recognition using WiFi Channel State Information
Authors:	陳紘淜;Chen, Hon-Pong
Contributors:	資訊工程學系
Keywords:	人類行為辨識;WiFi訊號;深度學習;CSI;HAR;LSTM;Wi-Fi
Date:	2023-07-26
Issue Date:	2024-09-19 16:52:39 (UTC+8)
Publisher:	國立中央大學
Abstract:	人體活動辨識 (Human Activity Recognition, HAR) 在健康照護、運動分析和輔助生活等各個領域中扮演著重要的角色。隨著Wi-Fi技術的普及，Wi-Fi通道狀態資訊 (Wi-Fi Channel State Information, CSI) 因其非侵入性的特性和廣泛可用性而成為HAR的寶貴資源。本研究探討了在HAR中應用深度學習技術，利用Wi-Fi CSI進行活動辨識。系統架構包含對CSI資訊進行預處理、特徵提取模組從CSI數據中提取相關特徵，以及使用深度學習模型 (如LSTM) 進行活動辨識的分類模組。本研究的發現有助於推進使用Wi-Fi CSI的HAR技術，並為發展堅固且即時的活動辨識系統提供了深入洞察。;Human Activity Recognition (HAR) plays a vital role in various domains such as healthcare, sports analysis, and assisted living. With the proliferation of Wi-Fi technology, Wi-Fi Channel State Information (CSI) has emerged as a valuable resource for HAR due to its non-intrusive nature and widespread availability. In this study, we investigate the application of deep learning techniques in HAR using Wi-Fi CSI. The system′s architecture consists of preprocesses CSI information, a feature extraction module that extracts relevant features from the CSI data, and a classification module that utilizes a deep learning model, such as LSTM, to perform activity recognition. The findings of this study contribute to the advancement of HAR techniques using Wi-Fi CSI and provide insights into the development of robust and real-time activity recognition systems.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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