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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Xiao-He Wang | en |
| dc.contributor.author | 王霄鶴 | zh_TW |
| dc.date.accessioned | 2022-11-25T07:45:51Z | - |
| dc.date.available | 2024-08-17 | |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-17 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82502 | - |
| dc.description.abstract | 復健医学致力于帮助患者面对导致残疾的损伤或疾病，在有限的范围内发挥最大的功能。患者在復健過程中，追求快速恢復的同時，常會忽略肌肉的疲勞而進行高強度的訓練。這樣的情況則可能會削弱複健療程的效果，甚至有可能危害到患者的人身安全。因此，即時檢測患者肌肉的疲勞狀態，可以更好地説明醫師瞭解患者目前肌肉情況，給予更客觀的資料供醫師參考。 由於通過採血檢測血液中的血乳酸是否快速地增加來判斷患者是否肌肉疲勞，會對患者造成侵入式的影響。而通過運動自覺量表（RPE）判斷肌肉疲勞，則會較大程度上受到患者主觀因素的干擾，同時具有較差的即時性。因此，近年來通過表面肌電信號（sEMG）來監測肌肉疲勞狀態的方法被大家廣泛使用。 於相關文獻中，仍有部分研究者選擇通過表面肌電信號監測肌肉在等長收縮而非等張收縮狀態下的疲勞狀態。而在肌肉等張收縮狀態下，如何通過表面肌電信號來檢測肌肉的啟動，以及在後續的信號處理過程上均會有更大的挑戰性。此外，大多數文獻選擇判斷肌肉是否進入疲勞狀態的依據是：（1）受試者聲稱自己疲勞；（2）受試者在運動過程中有明顯的姿勢變形；（3）受試者不能按要求的速率繼續運動。但在運動過程中，除了肌肉疲勞外，患者的注意力等問題也會導致上述情況。 基於以上原因，本文提出了一種基於表面肌電信號針對肌肉等張收縮的疲勞監測系統。通過TKE運算元及高解析度時頻分析的方法處理表面肌電信號，並配合機器學習方法分析和判斷肌肉疲勞狀態。同時，也將輔以非侵入式的腦電設備（EEG）監測持續注意力狀態，以減少持續注意力狀態對肌肉疲勞偵測造成的干擾，增加監測系統的準確性。在最終實驗中，十位受試者在同時佩戴肌電信號及腦波信號設備狀態下進行動作，證明本文所提出的方法可以更精准地即時檢測肌肉等張收縮疲勞狀態，並可以準確排除持續注意力下降等問題對系統的影響。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T07:45:51Z (GMT). No. of bitstreams: 1 U0001-1708202119063800.pdf: 4509736 bytes, checksum: ea826cf6a1d5eb615194ac00acabc447 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 # 誌謝 i 中文摘要 ii ABSTRACT iv CONTENTS vi TABLE OF ACRONYMS ix LIST OF FIGURES xii LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Survey 4 1.3 Contribution 6 1.4 Thesis Organization 8 Chapter 2 System Overview and Preliminaries 9 2.1 Muscle Contractions 9 2.1.1 Upper Limb Muscles 9 2.1.2 Muscle Contraction 11 2.2 Introduction and Collection of sEMG 13 2.2.1 Introduction of sEMG 13 2.2.2 sEMG Collection Instrument 16 2.3 Introduction and Collection of EEG 17 2.3.1 Introduction of EEG 18 2.3.2 EEG Collection Instrument 21 2.4 Time-Frequency Distributions (TFDs) 23 2.4.1 Continuous Wavelet Transform (CWT) 23 2.4.2 Stockwell Transform (ST) 25 2.5 Machine Learning Model 27 2.5.1 Naive Bayes 27 2.5.2 Support Vector Machine (SVM) 29 2.5.3 Artificial Neural Network (ANN) 30 Chapter 3 Design Muscle Fatigue Predict System 33 3.1 Overview of System Block Diagram 33 3.2 Fatigue Performance Detection Based on sEMG 35 3.2.1 sEMG Signal Acquisition and Preprocessing 35 3.2.2 sEMG Signal Active Detection 38 3.2.3 Time-Frequency Distributions (TFDs) of sEMG 41 3.2.4 Classification by Machine Learning Method 49 3.3 Sustained Attention Monitoring Based on EEG 54 3.3.1 EEG Signal Acquisition and Preprocessing 54 3.3.2 Feature Selection of EEG and Classification by Support Vector Machine 56 3.4 Hybrid System Integration Algorithm 60 Chapter 4 Experimental and Results 63 4.1 Offline Performance 63 4.1.1 Experiments of Using sEMG Detect Fatigue Performance 63 4.1.2 Experiments of Using EEG Detect Sustained Attention Decrease 67 4.2 Evaluate Experiment and Real-time Performance 68 4.2.1 Evaluate the Accuracy of the System in Detecting Sustained Attention Decrease 68 4.2.2 Evaluate the Accuracy of the System in Detecting Muscle Fatigue and the System Eliminates the interference of Sustained Attention Decrease 70 4.2.3 Evaluate the Accuracy of the System in Detecting Muscle Fatigue and Sustained Attention Decrease 73 Chapter 5 Conclusion 75 REFERENCE 77 | |
| dc.language.iso | zh-TW | |
| dc.subject | 腦電信號 | zh_TW |
| dc.subject | 肌肉疲勞分析 | zh_TW |
| dc.subject | 表面肌電信號 | zh_TW |
| dc.subject | 時頻分析 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | time-frequency analysis | en |
| dc.subject | muscle fatigue analysis | en |
| dc.subject | surface electromyography (sEMG) | en |
| dc.subject | electroencephalogram (EEG) | en |
| dc.subject | machine learning | en |
| dc.title | 以腦波輔助表面肌電圖偵測肌肉疲勞系統 | zh_TW |
| dc.title | Muscle Fatigue Detection System Based on sEMG Assisted with EEG | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧璐(Hsin-Tsai Liu),陳文翔(Chih-Yang Tseng),劉浩澧,賴金鑫 | |
| dc.subject.keyword | 肌肉疲勞分析,表面肌電信號,腦電信號,時頻分析,機器學習, | zh_TW |
| dc.subject.keyword | muscle fatigue analysis,surface electromyography (sEMG),electroencephalogram (EEG),time-frequency analysis,machine learning, | en |
| dc.relation.page | 82 | |
| dc.identifier.doi | 10.6342/NTU202102443 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-08-17 | - |
| 顯示於系所單位： | 電機工程學系 | |
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