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系統識別號 U0026-1907201312305600
論文名稱(中文) 基於HRV心律變異性分析之熱量消耗估測演算法及其於iPhone手機平台之實現
論文名稱(英文) Development of an Energy Expenditure Estimation Algorithm based on HRV Analysis and Its Realization on iPhone Platform
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 2
出版年 102
研究生(中文) 翁啟翔
研究生(英文) Chi-Hsiang Weng
學號 n26004278
學位類別 碩士
語文別 中文
論文頁數 86頁
口試委員 指導教授-王振興
口試委員-楊延光
口試委員-徐崇堯
中文關鍵字 熱量消耗  心律儲備  心律變異性  心跳傳輸協定  iPhone 
英文關鍵字 Energy Expenditure  Heart Rate Reserve  Heart Rate Variability  Heart Rate Profile  iPhone 
學科別分類
中文摘要 本論文基於心跳及脈搏訊號,開發基於活動強度分類及心律變異性特徵之熱量消耗估測演算法,並將此熱量消耗估測演算法及人機介面實現於iPhone手機上。本論文使用耳道式心跳感測器作為心律變異性分析之訊號來源,此耳道式心跳感測器可透過光學感測器偵測人體之脈搏,每次的脈搏資訊將經由藍芽4.0的心跳傳輸規範(heart rate profile, HRP)傳輸至iPhone手機中,經過iPhone運算過後,可在iPhone上呈現即時心跳、運動強度、熱量消耗等相關結果。其中運動強度、熱量消耗之結果乃基於本論文開發之活動強度分類及心律變異性特徵之熱量消耗估測演算法,此演算法由下列程序所組成:心律變異性特徵產生、活動強度分類、基於心律變異性特徵之熱量估測迴歸方程式。在心律變異性特徵產生方面,以時域以及非線性的心律變異性特徵為主;而活動強度分類則以心律儲備(Heart Rate Reserve, HRR)的觀念來進行活動強度分類;最後在不同活動強度(靜止、輕度、中度、劇烈)下,各自產生相對應的熱量估測方程式。並透過實驗收案,來驗證本論文結合活動強度分類與熱量消耗估測模型之準確度;結合活動強度分類之熱量消耗估測的估計標準誤差為1.002METs,相較於未結合活動強度分類之估計標準誤差為1.2723METs,估測準確度之改善率為14.68%,當加入對應HRV特徵至不同活動強度之熱量估測模型後,其估計標準誤差為0.7126METs,其相較於未結合HRV特徵與未經強度分類之估計標準誤差,估測準確度之改善率為39.21%。最後於iPhone上實作此演算法,提供給使用者更便利的熱量消耗估測工具。
英文摘要 This thesis presents an energy expenditure analysis algorithm based on heart rate variability (HRV) analysis and its realization into an iPhone platform application (iPhone App). The proposed iPhone App can display the instant heart rate, activity intensity, and energy expenditure (calorie consumption) of users. The heart rate displayed in the iPhone App is obtained from an in-ear heart rate monitor (HRM) headset. The HRM headset detects heart rate based on the variation of Photoplethysmography (PPG) and transmits the heart rate to iPhone App via the heart rate profile (HRP) of the Bluetooth® 4.0 specification. The iPhone App displays the activity intensity and the energy expenditure via an energy expenditure algorithm based on HRV analysis. The proposed energy expenditure analysis algorithm consists of a HRV feature generation process, an activity intensity classification process, and energy expenditure estimation based on HRV analysis process. The HRV feature generation process is responsible for generating the time- and non-linear domain HRV indexes. The activity intensity classification process determines users’ activity intensity based on the heart rate reserve (HRR) concept. After activity intensity classification process, energy expenditure regression models based on HRV analysis will be generated for various activity intensities. Finally, the effectiveness of the proposed algorithm has been successfully validated through our experiment, the average standard error of estimation (SEE) of the proposed energy expenditure estimation with activity intensity classification was 1.00 METs, and the SEE of energy expenditure estimation without activity intensity classification was 1.27 METs, which was higher than the SEE with activity classification. The performance of improvement of EE estimation with activity intensity classification was 14.68%. If the HRV features in the energy expenditure estimation regression models are considered, the average SEE of energy expenditure estimation with activity intensity classification and HRV feature can be reduced to 0.71 METs. The performance improvement was about 30%.
論文目次 中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 x
第1章 緒論 1-1
1.1 研究背景與動機 1-1
1.2 文獻探討 1-3
1.3 研究目的 1-6
1.4 論文架構 1-8
第2章 基於脈搏訊號之iPhone熱量消耗分析系統 2-1
2.1 耳道式耳機型心跳紀錄器感測原理 2-2
2.2 基於耳道式耳機型PPG訊號分析HRV 2-3
2.2.1 PRV與HRV之關聯性 2-3
2.2.2 低功耗藍芽心跳傳輸協定(Heart Rate Profile, HRP) 2-5
2.3 iPhone熱量消耗分析系統設計 2-9
2.3.1 藍芽接收模組 2-9
2.3.2 訊號分析模組 2-11
2.3.3 熱量估測模組 2-15
2.3.4 使用者資訊模組 2-18
第3章 結合強度分類與HRV特徵之熱量消耗估測演算法 3-1
3.1 演算法流程 3-2
3.2 訊號前處理 3-2
3.3 基於心律變異性之特徵擷取 3-4
3.3.1 時域分析 3-5
3.3.2 頻域分析 3-6
3.3.3 非線性分析 3-7
3.4 活動強度分類模型 3-8
3.5 熱量映射迴歸方程式 3-12
第4章 實驗架構與流程 4-1
4.1 實驗設備介紹 4-1
4.1.1 心電感測模組(Portable ECG Recorder) 4-2
4.1.2 K4b2氣體分析儀(Cardio Pulmonary Exercise Module) 4-2
4.2 實驗環境建置與資料收集 4-3
第5章 實驗結果與討論 5-1
5.1 活動強度分類實驗結果 5-1
5.2 熱量映射迴歸方程式實驗結果 5-3
5.3 iPhone熱量消耗分析系統呈現結果 5-15
5.4 實驗結果討論 5-18
第6章 結論與未來工作 6-1
6.1 結論 6-1
6.2 未來工作 6-2
參考文獻 7-1
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