在現今社會中，運動風氣日漸盛行，但在什麼樣的運動狀態下才能夠最有成效並非人人都了解；在專業運動員訓練中，需要取得運動員體能素質之相關指標數據，以利調整對運動員最佳的訓練方式；而在醫學領域研究中，許多研究也會以人體素質指標作分析及研究，人體素質指標有如疲勞(Fatigue)、無氧閾值(Anaerobic Threshold, AT)、最大氧氣攝取量 (Maximum Oxygen Uptake, VO2max)、最大心臟功率 (Max Cardiac Output)等，這些指標不論在訓練運動員或醫學領域相關性研究，皆希望能準確取得這些數據以達到各項目的之最佳成果。

要快速且準確地估算出人體素質指標必須先讓心率以線性關聯度提升。因此本文提出RRI回饋控制方式，依據不同的體能狀態調整運動強度，改善人體自我調節因素所造成之平緩曲線，提高心率與線性的關聯度，也將使人體數質指標估測值更加準確。

本文提出之RRI控制方式，可於未來醫學、人體體能研究領域方面取得更加正確的人體素質指標，使其相關研究之結果準確度更高。於訓練專業運動員中，使用此方式測量之數據協助其達到各項訓練之最佳訓練方式，使得運動員體能素質經過最佳訓練而更加有效率的提升。於一般人中，使用此控制方式來做心率控制或體能鍛鍊，也都能較佳的提升其鍛鍊成果。

In society, The sport is growing in popularity, But not everyone understands what kind of state of motion to get the best results. For the exercise of professional athletes, they need to get physical fitness indicator data to help them adjust workout intensity. For Medical Research, Physical fitness indicator data is also important information for analysis and research. Physical fitness indicator data such as Fatigue, AT (Anaerobic Threshold), VO2max (Maximum Oxygen Uptake), Maximum Cardiac Output, etc. Whether exercise of professional athletes or medical research are hoping to get accurate data to help them achieve the best results.In order to quickly and accurately estimate the physical fitness indicator data. First of all, Heart rate must be linear correlation degree rise. In this paper, we use the RRI feedback control scheme. According to each person's physical fitness to adjust the exercise intensity, improve people's self-regulating factors caused gentle curve, increase the heart rate and linear correlation degree, physical fitness indicator measuring data will also be more accurate. In this paper, Proposed use of RRI feedback control can be more accurately measured physical fitness indicator data. In the future, Whether medical science research, human physical research or training professional athletes are able to achieve more accurate results.

目錄
誌謝	I
摘要	II
Abstract	III
目錄	IV
圖目錄	VI
表目錄	VIII
第一章 緒論	1
1.1 研究動機與目的	1
1.2 論文架構	3
第二章 研究背景簡介	4
2.1 技術簡介	4
2.1.1 人體素質指標簡介	4
2.1.1.1 疲勞	4
2.1.1.2 無氧閾值	5
2.1.1.3 最大攝氧量	6
2.1.1.4 最大心輸出量	7
2.1.2 藍牙技術簡介	8
2.1.3 穿戴式裝置簡介	9
2.1.4 智慧型行動裝置簡介	10
2.2 系統架構	12
2.2.1 系統介紹	12
2.2.2 硬體設備	13
2.2.2.1 心率感測器	13
2.2.2.2 智慧型行動裝置	15
2.2.3軟體平台	16
第三章 實驗方法	18
3.1 RRI回饋控制	18
3.2 系統測試流程	20
第四章 測量結果與分析討論	27
4.1測量結果	27
第五章 結論	33
5.1 研究結果	33
5.2 未來展望	33
參考文獻	34

 
圖目錄
圖1.1 系統架構圖	2
圖2.1 BLE Service、Characteristic狀態	9
圖2.2 系統方塊圖	13
圖2.3 ECG250心率感測器	15
圖2.4 心跳帶配戴示意圖	15
圖2.5 Samsung Galaxy Note3	16
圖2.6 APP流程圖	17
圖3.1 第一階段測試之程式流程圖	19
圖3.2 第二階段程式流程圖	20
圖3.3 APP左側選單	22
圖3.4 搜尋跑步機裝置	22
圖3.5 與跑步機連線成功之MAC Address訊息	23
圖3.6 搜尋Heart Rate Sensor	23
圖3.7 與Heart Rate Sensor連線成功之MAC Address訊息	24
圖3.8 第一階段之靜態測試	24
圖3.9第一階段之動態測試	25
圖3.10 第一階段測試完成	25
圖3.11 第一階段靜態測試實測圖	26
圖3.12 第一階段動態測試實測圖	26
圖3.13 第二階段訓練類型之設定	26
圖4.1 受測者1之等功率加速實測測量結果	28
圖4.2 受測者1之Bruce Protocol實測測量結果	28
圖4.3 受測者1之RRI回饋控制實測測量結果	29
圖4.4 受測者2之等功率加速實測測量結果	29
圖4.5 受測者2之Bruce Protocol實測測量結果	30
圖4.6 受測者2之RRI回饋控制實測測量結果	30
圖4.7 兩位受測者於三種測量模式測量結果之Correlation分析	31
圖4.8 兩位受測者於三種測量模式測量結果之MAD分析	31
圖4.9 兩位受測者於三種測量模式測量結果之MAPE分析	32

 
表目錄
表4.1 Correlation分析之結果	32
表4.2 MAD分析之結果	32
表4.3 MAPE分析之結果	32

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