Real-time Human Activity Recognition using WiFi Channel State Information

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姓名

陳紘淜(Hon-Pong Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Real-time Human Activity Recognition using WiFi Channel State Information)

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至系統瀏覽論文 (2025-7-5以後開放)

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 人類行為辨識
★ WiFi訊號
★ 深度學習

關鍵字(英)

★ CSI
★ HAR
★ LSTM
★ Wi-Fi

論文目次

CHAPTER I. INTRODUCTION 1
CHAPTER II. BACKGROUND 4
2-1. EXPLORING THE RESEARCH ADVANTAGES OF WI-FI IMPLEMENTATION 4
2-2. WI-FI FREQUENCY AND 802.11N 4
2-3. INTRODUCE CHANNEL STATE INFORMATION(CSI) 6
2-4. WI-FI CSI ROLE IN DEEP LEARNING 8
2-4-1 Respiration Monitoring: 8
2-4-2 Wi-Fi CSI-Based Indoor Localization: 9
2-4-3 Fall Detection: 9
2-5. SEQUENCE MODEL 10
CHAPTER III. SYSTEM AND IMPLEMENT 13
3-1. PREPROCESS 14
3-1-1. Hampel filter 14
3-1-2. DWT denoise 17
3-2. AUGMENTATION 20
3-2-1. DROPOUT 20
3-3. ML MODEL 21
3-3-1. LSTM 21
3-1-2. Attention Mechanism 23
CHAPTER IV. EVALUATION AND RESULTS 25
4-1. EXPERIMENTAL SETUP 25
4-1-1. DATASET 25
4-1-2. EVALUATION 30
4-1-3. Hardware and Setup 30
4-1-4. Model and Training process setup 31
4-2. RESULTS 33
4-2-1 LSTM WITH ATTENTION 33
CHAPTER V. CONCLUSION 41
REFERENCE 43

參考文獻

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指導教授

施國琛林智揚

審核日期

2023-7-26

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