臺灣博碩士論文加值系統

各大醫院的急診室，總是有人滿為患的問題。管理龐大的人流，便成為一件需要關注的問題。此外，來到急診室的人容易因為醫院複雜的環境及醫療設備而迷失方向，這也使得急診室更加壅塞。在此研究中，我們透過低功率藍芽技術與智慧型手機，結合台大醫院的醫療資訊系統，實作室內導航功能，讓使用者清楚的知道目前在急診室的哪個位置以及如何前往目的地，並讓醫護人員能夠隨時追蹤病人在急診室內的位置。另外，我們也建立了醫護人員端與病患端的訊息溝通系統，以提供即時的急診醫療與照護服務。
本系統的定位功能是基於低功率藍芽無線傳輸技術、慣性感應元件，以接收藍芽訊號強度搭配慣性移動，定位出病患在急診室中的位置。在醫院急診室環境中測試，本系統的平均誤差可以達到1.48公尺。
透過本系統之室內導航功能，可以解決因為在急診室中的人迷路，所造成的壅塞問題。並藉由獨立的溝通平台，提供具隱私性的管道，提升醫師、護理師與病人之間的互動關係。

Emergency department usually has the problem of overcrowding. Thus how to manage the large volumes of visitors has become a thorny problem. Furthermore, the people who come to emergency department for the first time have a tendency to get lost, this makes the overcrowding problem more serious. In this study, we implement the indoor navigation system by using the Bluetooth Low Energy (BLE) technology, smartphone, and combining HIS service. The system is to make the user clearly knows where he is and how to go to the destination and let the medical staff track the location of patients at any time. Besides, we also established a caregiver-patient communication system to provide seamless emergency service.
The indoor positioning system uses the Received Signal Strength (RSS) and inertial movement which are based on Bluetooth Low Energy (BLE) technology and inertial sensors to estimate the user''s location in the emergency department. Several experiments were conducted in the emergency department, the mean error of our proposed localization algorithm can achieve 1.48 meter.
By the proposed indoor navigation system, the problem of getting lost in the emergency department can be alleviated. Besides, the individual communication platform provides the patients, doctors, and nurses convenient and private communication channels.

口試委員會審定書 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 3
Chapter 2 Literature Review 5
2.1 Indoor Localization 5
Chapter 3 Methodology 7
3.1 Indoor Localization 8
3.2 iBeacon-Based Localization 9
3.2.1 Introduction to iBeacon 9
3.2.2 Kalman Filter 11
3.2.3 Weighted Centroid Localization 13
3.3 IMU-Based Localization 15
3.3.1 Heading Direction 17
3.3.2 Step Detection 19
3.3.3 Step Length 22
3.4 Integrated Localization 23
3.5 Route Guidance 24
3.6 The Patient’s Mobile APP 27
3.6.1 Patient-Caregiver Communication 28
3.6.2 Indoor navigation 29
3.7 Medical Staff’s Side 31
3.7.1 Non-Response Request 31
3.7.2 Patient List 33
Chapter 4 Result 35
4.1 Experiment Environment 35
4.2 Experiment Result 35
Chapter 5 Conclusion and Future Work 38
REFERENCE 39

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