系統識別號 | U0002-2407201301310800 |
---|---|
DOI | 10.6846/TKU.2013.00993 |
論文名稱(中文) | 微型無線生理感測器融合整合通訊與醫療物聯網 |
論文名稱(英文) | Integrating Unified Communications and Internet of m-health Things with micro wireless physiological sensors |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 資訊工程學系碩士在職專班 |
系所名稱(英文) | Department of Computer Science and Information Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 101 |
學期 | 2 |
出版年 | 102 |
研究生(中文) | 何信凱 |
研究生(英文) | Hsin-Kai Ho |
學號 | 700410235 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | 英文 |
口試日期 | 2013-06-14 |
論文頁數 | 88頁 |
口試委員 |
指導教授
-
葛煥昭(keh@cs.tku.edu.tw)
委員 - 施俊哲(ccshih@vghtpe.gov.tw) 委員 - 顏淑惠(105390@mail.tku.edu.tw) 委員 - 葛煥昭(keh@cs.tku.edu.tw) |
關鍵字(中) |
整合通訊 物聯網 無線生理感測器 行動照護 |
關鍵字(英) |
Unified Communications Internet of Things Wireless physiological sensors Mobile healthcare |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
本研究建構整合通訊平台(Unified Communications, UC)融合於醫療物聯網 (Internet of m-health Things, m-IoT)架構下應用於行動醫療照護領域,利用微型無線生理感測器結合智慧型行動裝置,得以實行病患之監護、急救、追蹤、分析、診斷、預警、定址服務與協同醫療照護。使資料、語音、影像、多媒體訊息元素匯流於無線近身網路、無線感測網路、無線區域網路、網際網路、公共交換電話網路及社群網路之中,形塑出無縫兼容(Ubiquitous)的網路架構。使得被看護者的生命跡象數據(Vital Signs),包含了心跳率、呼吸率、心電圖、體溫、姿勢、活動率、定位位置、現場街景圖、現場背景錄音回饋,即時融合於異質平台以進行相關分析、運算、統計及比對。 整合通訊架構下可確保看護者能隨時隨地透過任何通訊工具、媒體、裝置、平台掌握到被看護者狀況,即時給予適當的醫療診斷或進行緊急救援,醫療照護不再因距離、空間、時間而有所受限。本研究並改良了傳統行動照護的通訊架構、監測模式及預警機制並且提供一個有效、即時、多通道、群組廣播及雙向之通訊架構,並減少協同醫療照護的人力成本且提高通訊服務效益。希冀導入整合通訊相關技術提高行動照護品質,進而將通訊產業打開更廣泛的應用領域及研究突破,略盡一己之力。 |
英文摘要 |
This study establishes Unified Communications (UC) platform integrated with Internet of m-health Things (m-IoT) architecture, which can be applied to healthcare. Using micro wireless physiological sensors combined with smart mobile devices, that is able to implement patients immediate monitoring, first aid, tracking, analysis, diagnosis, alarm-triggering, locating and collaboration with medical healthcare. Converging data, voice, video, multimedia messaging elements into WBAN, WSN, WLAN, Internet, PSTN and Social Networks, which integrates vital signs, including heart rate, respiratory rate, ECG, body temperature, posture, activity rate, GPS location, street scene and live background recording feedback into different heterogeneous platforms to make a seamless compatibility ubiquitous architecture, and thus it conducts correlation analysis, computing, statistics and comparison. In this communications architecture, it ensures that caregivers could monitor patients at anytime and anywhere through any communication tools, media, devices and platforms. Caregivers could instantly provide proper medical diagnosis or emergency medical care without limited to distance, space and time. This study improved the communication architecture of traditional mobile healthcare, monitoring methods and warning mechanisms, which also offers effective, real-time, multi-channel, group broadcasting and two-way communication to reduce cooperative medical care cost and enhance the effectiveness of communications services. Looking to introduce Unified Communications technologies for mobile healthcare in order to increase mobile medical service quality, and then open a wider range of applications and research breakthroughs. |
第三語言摘要 | |
論文目次 |
目錄 目錄 III 圖目錄 VI 表目錄 IX 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的與方法 1 1.3 研究架構 2 1.4 研究貢獻 4 第二章 相關研究 6 2.1 整合通訊概觀 6 2.1.1 VoiceXML 技術 7 2.1.2 通訊協定架構 9 2.1.3 協同作業模式 13 2.1.4 醫療通訊產業的應用效益 13 2.2 物聯網的發展 14 2.2.1 關鍵技術及架構 14 2.2.2 物聯網感測閘道器(WSN-IoT Gateway) 15 2.2.3 各國醫療物聯網現況 16 2.3 感測器 17 2.3.1 感測器元件 17 2.3.2 微機電系統(Micro-Electromechanical Systems, MEMS) 18 2.3.3 醫療無線感測裝置 20 2.4 無線近身網路介紹 22 2.4.1 WBAN 通訊架構 22 2.4.2 WBAN 的挑戰 22 2.4.3 無線通訊技術 24 2.5 生命跡象 28 2.5.1 ECG 的介紹 29 2.5.2 自律神經系統(Autonomic Nervous System, ANS) 30 2.5.3 心率變異分析(Heart Rate Variability, HRV) 31 2.6 文獻探討 33 第三章 系統架構與設計 35 3.1 整合通訊融合醫療物聯網 35 3.1.1 系統架構 36 3.1.2 元件框架 36 3.1.3 通訊循序狀態 39 3.2 感測層(Sensor Layer) 40 3.2.1 感測擷取模組(Sensor Collection Module, SCM) 40 3.2.2 感測傳輸模組(Sensor Transmission Module, STM) 42 3.3 中介層(Middleware Layer) 44 3.3.1 整合通訊閘道器(Unified Communication Gateway, UCG) 44 3.3.2 語音流程模組(Voice Flow Module, VFM) 44 3.3.3 即時通訊模組(Instant Messaging Module, IMM) 45 3.3.4 社群網路通訊模組(Social Networking Module, SNM) 48 3.3.5 電子告示版模組(LED Module, LM) 49 3.3.6 語音辨識模組(Automatic Speech Recognition Module, ASRM) 50 3.4 應用層(Application Layer) 52 3.5 偵測告警流程(Detector and Alerting Flow) 52 第四章 實作結果與分析 56 4.1 行動生理量測 56 4.2 遠端生命跡象儀表板 57 4.3 即時生理監測記錄 57 4.4 整合通訊管道 61 4.5 生理數據分析 62 4.6 疾病追蹤及評估 65 第五章 結論與未來研究 68 5.1 結論 68 5.2 未來研究 68 參考文獻 70 附錄-英文論文 79 圖目錄 圖1.1: 全球高齡人口分佈比例(現況及未來) [1] 1 圖1.2: 研究架構魚骨圖 3 圖2.1: 整合通訊平台架構圖[2] 7 圖2.2: VoiceXML 架構圖[3] 8 圖2.3: VoiceXML 資源控制與邏輯流程圖[4] 8 圖2.4: VoiceXML 語音介面框架圖[5] 10 圖2.5: H.323 通訊協定架構[6] 11 圖2.6: SIP 通訊協定架構[6] 12 圖2.7: VoiceXML 整合SIP 協定通訊架構[7] 12 圖2.8: 協同通訊擴展示意圖[8] 13 圖2.9: 物聯網架構概念圖[9] 15 圖2.10: WSN-IoT 閘道器系統架構[10] 16 圖2.11: 感測元件圖[11] 17 圖2.12: SoC/SiP 建構無線醫療裝置[12] 19 圖2.13: 生理感測器整合設計架構[13] 19 圖2.14: 人體生理訊號偵測節點[14] 20 圖2.15: 無線感測器種類、測量方式及資料速率[15] 21 圖2.16: 生理監測分類[16] 21 圖2.17: WBAN 通訊示意圖-1 [14] 23 圖2.18: WBAN 通訊示意圖-2 [17] 23 圖2.19: WSN/WBAN 的挑戰差異[14] 24 圖2.20: 無線網路技術傳輸距離/速度比較圖24 圖2.21: ZigBee 協定堆疊[18] 25 圖2.22: BLE 協定堆疊[19] 26 圖2.23: Bluetooth/UWB/ZigBee/Wi-Fi Protocols 比較[20] 28 圖2.24: ECG 波形訊號[21] 29 圖2.25: 自律神經系統[22] 31 圖2.26: R-R 訊號 32 圖2.27: 心率變異分析流程圖[23] 32 圖3.1: 系統網路涵蓋圖 35 圖3.2: 系統架構示意圖 37 圖3.3: 元件框架設計圖 38 圖3.4: 通訊循序狀態圖 39 圖3.5: 感測層硬體抽象架構圖 41 圖3.6: Android Eclipse 開發畫面 41 圖3.7: SOAP 定義傳遞要求及回應的XML 格式 43 圖3.8: E-R 圖 43 圖3.9: 中介層示意圖 44 圖3.10: 整合通訊閘通器模組圖 45 圖3.11: 類比語音卡片模組[24] 46 圖3.12: IVR 語音流程 46 圖3.13: Voice Flow Module -物件類別模型圖 47 圖3.14: Instant Messaging Module -物件類別模型圖 47 圖3.15: Facebook 整合流程圖[25] 48 圖3.16: Social Networking Module -物件類別模型圖 48 圖3.17: Oauth 認證流程[26] 49 圖3.18: LED 電子看版硬體規格 49 圖3.19: LED Module -物件類別模型圖 50 圖3.20: ASR 語音辨識引擎架構[27] 51 圖3.21: ASR 語音辨識文法規格 51 圖3.22: ASR Module -物件類別模型圖 52 圖3.23: MVVM 模型框架[28] 53 圖3.24: 偵測告警流程圖 55 圖4.1: 行動生理量測介面 56 圖4.2: 遠端生理監控儀表板 57 圖4.3: 生理監測記錄 58 圖4.4: 現場背景錄音回饋 58 圖4.5: 心跳率即時監測 58 圖4.6: 呼吸率即時監測 59 圖4.7: 體溫即時監測 59 圖4.8: 姿勢即時監測 59 圖4.9: 活動狀態即時監測 60 圖4.10: OLAP 分析 60 圖4.11: 整合通訊管道 61 圖4.12: 心跳率/吸呼率-量測結果分析圖 62 圖4.13: 心跳率/吸呼率/HRV-量測結果分析圖 63 圖4.14: 吸呼率/RR-量測結果分析圖 63 圖4.15: RR-量測結果分析圖 64 圖4.16: ECG-量測結果分析圖 64 圖4.17: 三軸加速度器-量測結果分析圖 65 圖4.18: 原始生理數據 66 圖4.19: 類神經網路探勘模型 66 圖4.20: 決策樹探勘模型 67 圖4.21: 集群探勘分析模型 67 表目錄 表3.1: 藍芽通訊資料封包協議 40 |
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