臺灣博碩士論文加值系統

隨著醫療進步，人口老化比例不斷上升。但由於少子化，許多家庭因為經濟壓力而選擇出去工作，導致長輩獨自在家，一旦長輩身體突然出現問題，無法馬上知道。所以如何運用物聯網(Internet Of Things，IOT)結合其它技術監控長輩獨自在家的生活情況為現今重要的議題。本論文設計一居家照護系統，可從此系統得知長輩生理資訊以及位置資訊，其中生理資訊的部分包含量測血氧脈搏數值和服藥資訊，服藥資訊部分為透過影像辨識(Image Recognition)技術去實現，使用Yolov3做藥物辨識。位置資訊部分透過Wi-Fi無線傳輸訊號來做室內定位(Indoor Positioning)，量測長輩與無線訊號發送端距離的接受訊號強度(RSSI)，透過量測完的結果去判斷長輩的位置。接下來經由MQTT傳送生理資訊和位置資訊到Node-RED平台上，並在Node-RED平台完成Line通知功能、儲存資料庫、顯示老人生理和位置資訊在老人狀況儀表板、實作查詢介面。其中Line通知功能包括血氧脈搏數值異常危險通知、位置通知、長輩服藥通知、定時提醒用藥通知，儲存資料庫為方便後面查詢介面可用，查詢介面為方便家屬可以選擇想要知道的資訊去查詢。透過Node-RED可以將量測血氧脈搏、室內定位、藥物影像辨識三個應用整合在一起並實現通知、儲存、顯示、查詢這四個功能，完成多功能居家照護系統。藉此應用之完成，希望能減輕社會長照需求，造福社會。

With the advancement of medical care, the proportion of the aging population continues to rise. However, due to the declining birthrate, many families choose to go out to work because of financial pressure, resulting in the elders being at home alone. Once the elders suddenly have physical problems, they cannot know immediately. Therefore, how to use the Internet of Things (IOT) combined with other technologies to monitor the living conditions of the elders at home alone is an important issue today. In this paper, a home care system is designed, from which the physiological information and location information of the elders can be obtained. The physiological information part includes the measurement of blood oxygen and pulse value and the medication information. The medication information part is realized through Image Recognition technology，by using Yolov3 for drug identification. The location information part uses Wi-Fi wireless transmission signals to do indoor positioning (Indoor Positioning), measures the received signal strength (RSSI) of the distance between the elder and the wireless signal sender, and judges the elder's location through the measured results. Next, send the physiological information and location information to the Node-RED platform via MQTT, and complete the Line notification function on the Node-red platform, store the database, display the elderly's physiological and location information on the elderly's condition dashboard, and implement the query interface. The Line notification function includes abnormal blood oxygen and pulse value danger notification, location notification, elder medication notification, and regular medication reminder notification. The storage database is convenient for later query interface, and the query interface is convenient for family members to choose the information they want to know to query. Through Node-RED, three applications of measuring blood oxygen and pulse, indoor positioning, and drug image recognition can be integrated to realize the four functions of notification, storage, display, and query to complete a multi-functional home care system. With the completion of this application, it is hoped that the long-term care needs of the society will be alleviated and the society will benefit.

摘要........... i
Abstract....... ii
誌謝........... iii
目錄........... iv
表目錄.......... vi
圖目錄.......... vii
第一章 緒論..... 1
1.1 研究背景.... ........1
1.2 研究動機與目的...... 2
1.3 論文架構............ 2
第二章 文獻回顧......... 3
2.1 物聯網............. 3
2.2 居家照護系統........ 5
2.3 MQTT............... 6
2.4 Node-RED........... 8
2.5 資料庫.............. 9
2.6 室內定位............ 10
2.6.1 室內定位方法...... 11
2.6.2 RSSI測量......... 11
2.7 卷積神經網路........ 12
2.8 物件偵測............ 15
2.8.1 Yolo介紹......... 16
2.8.2 Yolo運算過程和神經網路..... 17
第三章 系統規劃與製作............ 19
3.1 軟硬體介紹.......... 19
3.2 系統規劃............ 26
3.2.1 系統流程.......... 26
3.2.2 系統架構.......... 27
3.2.3 MQTT Broker...... 27
3.3 血氧脈搏量測實驗.... 29
3.3.1 MAX30100感測模組和MQTT執行流程..... 29
3.3.2 血氧脈搏資料庫.................... 30
3.3.3 量測血氧脈搏實驗Node-RED流程....... 32
3.4 簡易室內定位實驗.................... 33
3.4.1 實驗環境.......................... 33
3.4.2 RSSI訊號量測和MQTT執行流程......... 34
3.4.3 位置資料庫........................ 36
3.4.4 室內定位Node-RED流程.............. 37
3.5 藥物影像辨識實驗.................... 38
3.5.1 實驗環境......................... 39
3.5.2 資料收集與訓練前置作業............ 40
3.5.3 影像辨識和MQTT流程............... 43
3.5.4 模型評估方式..................... 45
3.5.5 藥物資料庫....................... 46
3.5.6 藥物影像辨識Node-RED流程.......... 47
第四章 實作成果......................... 49
4.1 實驗結果........................... 49
4.1.1 血氧脈搏量測實驗.................. 49
4.1.2 簡易室內定位實驗.................. 50
4.1.3 藥物影像辨識實驗.................. 52
4.2 使用者介面......................... 56
第五章 結論與未來研究................... 59
參考文獻............................... 60
Extended Abstract..................... 65

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