改善運動中手腕型PPG心率測量

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姓名

胡君陽(Jiun-Yang Hu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

改善運動中手腕型PPG心率測量
(Improvement of Wrist-worn Photoplethysmographic Heart Rate Monitoring during Physical Activities)

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摘要(中)

基於光體積描記法(Photoplethysmography, PPG)的手腕心率監測，使用者在靜態中測量會有較高的準確性，但是在動態中測量則會有較大的誤差。由於PPG訊號會受到手腕動作所引起的偽影雜訊干擾，影響心率測量的準確度。為了改善這個在動態中測量心率的問題，本研究提出一個方法由，來達到抑制動作偽影以及提高心率估計準確度的目的。這方法由兩階段的策略組成。第一階段分析由PPG訊號與三軸加速度訊號所得出的頻譜，利用所提出的抑制動作偽影方法識別出PPG頻譜上的偽影區間並抑制其偽影干擾。第二階段基於短時間內心率的連續性，利用前一刻的心率縮小心率估計的搜尋範圍，提高動態中PPG心率估計的準確性。12位受測者的心率估計誤差平均值為2.57 BPM，有效減少運動過程中所引起的心率測量上的失真。

摘要(英)

While the accuracy of heart rate monitoring using photoplethysmography (PPG) is quite acceptable when the person stays still, it could deteriorate during physical activities. The motion artifacts due to hand movements in the received PPG signal interfere with the heart rate detection. In this study, we propose a two-stage strategy to reject the motion artifacts and improve the accuracy of heart rate detection. At the first stage, the frequency spectrum of the received PPG signal is analyzed and the spectral components owing to the motion artifacts are identified and deleted by spectral subtraction. To detect the frequency spectrum of the motion signal, three-axis accelerometers are used. At the second stage, the heart rate is estimated on the basis of the spectral peaks of next moment and the tracking information of previous heart rates. This tracking estimation method has considerably improved the accuracy of PPG heart rate monitoring during physical activities. Experiments with 12 test subjects resulted in an average heart rate error of 2.57 BPM.

關鍵字(中)

★ 光體積描記法
★ 心率監測
★ 穿戴裝置
★ 動作偽影

關鍵字(英)

★ Photoplethysmography
★ Heart Rate Monitoring
★ Wearable Device
★ Motion Artifact

論文目次

摘要 v
Abstract vi
誌謝 vii
目錄 viii
圖目錄 xi
表目錄 xiii
第一章緒論 1
1.1 研究背景與動機 1
1.2 文獻探討 5
1.3 研究架構 7
第二章背景知識 8
2.1 心率與脈搏 8
2.2 心率訊號測量 10
2.2.1 心電圖(Electrocardiography, ECG) 11
2.2.2 光體積變化描記法 (Photoplethysmography, PPG) 14
2.3 動作訊號測量 18
2.3.1 前言 18
2.3.2 加速度感測器 18
2.4 藍芽 Bluetooh 21
2.5 時域處理介紹 23
2.5.1.奇異譜分析(Singular Spectrum Analysis, SSA) 23
2.5.2正規化最小均方(Normalized Least Mean Square，NLMS) 25
2.6 頻率域處理介紹 26
2.6.1快速傅立葉轉換(Fast Fourier Transform, FFT) 26
2.6.2短時距傅立葉變換(Short-Time Fourier Transform, STFT) 29
2.6.3譜減法介紹 30
第三章系統架構及軟、硬體架構介紹 31
3.1 系統架構 31
3.2 感測端系統架構 32
3.2.1感測端介紹 33
3.2.2接收端介紹 40
第四章實驗方法 41
4.1 抑制動作偽影 42
4.1.1判斷運動訊號的基頻與倍頻 42
4.1.2頻譜正規化 45
4.1.3頻譜功率保留 46
4.2 心率追蹤 50
4.2.1頻譜篩選 51
4.2.2峰值選擇 52
第五章實驗結果與討論 54
5.1 實驗資料介紹 54
5.1.1實驗過程介紹 54
5.2 方法實驗結果 56
5.2.1時域訊號 56
5.2.2頻域訊號 59
5.2.3抑制動作偽影結果 61
5.2.4心率追蹤結果 63
5.3 其他結果 66
第六章結論與未來展望 69
6.1 結論 69
6.2 未來展望 72
參考文獻 73
附錄1 77
附錄2 79

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指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2017-7-25

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