跳到主要內容

臺灣博碩士論文加值系統

(44.192.48.196) 您好!臺灣時間:2024/06/23 20:49
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:李士杰
研究生(外文):Li,Shih-Chieh
論文名稱:以物聯網技術建置長期照護感測訊號智能整合服務環境
論文名稱(外文):Using Internet of Things Technology to Construct an Integration of Intelligent Sensing Environment for Long-term Care Service
指導教授:洪論評洪論評引用關係
指導教授(外文):Hung,Lun-Ping
口試委員:林春成劉建良童恒新梅興洪論評
口試委員(外文):Lin,Chun-ChengLiu,Chien-LiangTung,Heng-HsinMei,HsingHung,Lun-Ping
口試日期:2020-07-28
學位類別:碩士
校院名稱:國立臺北護理健康大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:57
中文關鍵詞:物聯網長期照護多通道閘道器邊緣計算可變閾值
外文關鍵詞:Internet of ThingsLong-term CareMulti-Channel GatewayEdge ComputingVariable Threshold
相關次數:
  • 被引用被引用:0
  • 點閱點閱:197
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
全球高齡化議題逐漸沸騰,致使台灣人口結構急遽轉變,高齡者伴隨年紀增長失能率迅速攀升,亦迎來扶養比例倍增的問題,家屬除面對經濟負擔外,也無暇陪伴長輩,進而導致將長者安置於住宿型機構。傳統醫療照護模式在專業醫事人力匱乏及醫療資源限制上,無法有效集中管理,造成社會成本增高之困境。鑑於資通訊技術發展迅速,許多創新研發及跨領域應用,已能有效投入臨床機構場域之中,提升醫療效率與拓展政策範疇儼然成為重要關鍵。因此本研究將導入物聯網的基礎概念,運用生醫與環境感測模組結合多通訊傳輸技術,經由邊緣、雲計算鏈結階段式告警機制,深入至臨床場域實地部署,且加入指數加權移動平均法動態計算環境最適閾值,幫助照顧人員定期審視長輩自身狀況,建立妥適長期照護模式,共同創造保命、保健康、保品質之高齡照護生活環境,提高機構照護品質之成效,落實健康樂活之目標。
Because of the population ageing , the population structure changed a lot in Taiwan. The probability of incapacitation of elderly people and the old age dependency ratio are both increases greatly which results in the increasing numbers of elderly living in residential institutions because family members have no time to accompany elderly due to the problem of economic burden. Because the traditional health-care has limits of medical resource and lack of man power, can’t centralized management efficiently and cause the social cost increased. Given that technology of the information and communication growth rapidly, many inventions and the Cross-disciplinary application already involved into the environment of the Dementia-care. It is important to increase the medical treatment efficiency and expand the policy category. Thus this research will import the basic concept of IoT, biomedicine environment sensor module and Cross-communication transferring technology, after the edge and cloud calculation linked stage alarm mechanism, into the clinical field to set the devices and add the exponentially weighted moving average to calculate the most suitable threshold, this can help caregivers assist elders make self-observation to construct a long-term care system further. As mention above, it is possible to create a healthy, high-quality elderly care environment to improve the quality of care organization and implement the goal of health and happiness.
摘要 I
ABSTRACT II
目 錄 III
圖目錄 V
表目錄 VI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機及目的 3
1.3 研究流程 7
第二章 文獻探討 9
2.1 資通訊科技與醫療結合應用發展 9
2.2 多通訊閘道器設計相關研究 11
2.3 邊緣計算嵌入應用 12
2.4 動態閾值調整探討 14
第三章 研究方法 16
3.1 系統需求分析與設計 16
3.1.1 環境介紹 16
3.1.2 開發工具與無線傳輸微控模式研究 17
3.2 住宿型機構照護應用與規劃 18
3.2.1 生理量測設備 18
3.2.2 環境監測設備 19
3.3 閘道器數據身份認證應用 19
3.3.1 多通訊閘道器技術 19
3.3.2 數據傳輸身份認證機制 21
3.4 邊緣計算與告警閾值設計 23
3.4.1 多通訊邊緣計算模式 23
3.4.2 緊急事件告警閾值微調機制 25
3.5 健康資訊平台規劃 28
3.5.1 系統架構設計 29
3.5.2 相關技術工具 32
第四章 結果 34
4.1 實驗環境 34
4.2 系統測試結果 36
第五章 結論 42
參考文獻 44

1.國家發展委員會. 中華民國人口推估(107至154年). 2018; https://www.ndc.gov.tw/Content_List.aspx?n=84223C65B6F94D72.
2.內政部統計處, 109年第10週內政統計通報. 2019: p. 1-2.
3.衛生福利部社會及家庭署, 老人福利機構概況107-12. 2019: p. 1.
4.衛生福利部. 109 年度老人福利機構評鑑指標. 2020; https://www.rootlaw.com.tw/Attach/L-Doc/A040170021022200-1081212-9000-001.pdf.
5.衛生福利部. 長期照顧十年計畫2.0(106~115年)(核定本). 2017; https://1966.gov.tw/LTC/cp-4001-42414-201.html.
6.衛生福利部. 住宿式服務機構使用者補助方案. 2019; https://1966.gov.tw/LTC/lp-4511-201.html.
7.內政部, 火災災害防救業務計畫. 108: p. 71-91.
8.Montgomery, D.C., Introduction to Statistical Quality Control. 8th ed. 2019, New York, NY, USA: Wiley.
9.Hunter, J.S., The Exponentially Weighted Moving Average. Journal of Quality Technology, 1986. 18(4): p. 203-210.
10.Islam, S.M.R., et al., The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access, 2015. 3: p. 678-708.
11.Fanucci, L., et al., Sensing Devices and Sensor Signal Processing for Remote Monitoring of Vital Signs in CHF Patients. IEEE Transactions on Instrumentation and Measurement, 2013. 62(3): p. 553-569.
12.Ohmura, R., et al. B-Pack: a Bluetooth-based wearable sensing device for nursing activity recognition. in 2006 1st International Symposium on Wireless Pervasive Computing. 2006.
13.Singh, D. and R. Gour, An IoT Framework for Healthcare Monitoring Systems. International Journal of Computer Science and Information Security, IJCSIS ISSN 1947-5500, 2016. Vol. 14 No. 5: p. 6.
14.Bello, O. and S. Zeadally, Intelligent Device-to-Device Communication in the Internet of Things. IEEE Systems Journal, 2016. 10(3): p. 1172-1182.
15.El Zouka, H.A. and M.M. Hosni, Secure IoT communications for smart healthcare monitoring system. Internet of Things, 2019: p. 1-14.
16.Jo, J., et al., Development of an IoT-Based Indoor Air Quality Monitoring Platform. Journal of Sensors, 2020. 2020: p. 1-14.
17.Jayapal, C., et al., IoT-Based Real Time Air Pollution Monitoring System. International Journal of Grid and High Performance Computing, 2019. 11: p. 28-41.
18.Kodali, R.K., G. Swamy, and B. Lakshmi. An implementation of IoT for healthcare. in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS). 2015.
19.Kang, S., et al., Survey on the demand for adoption of Internet of Things (IoT)-based services in hospitals: Investigation of nurses' perception in a tertiary university hospital. Applied Nursing Research, 2019. 47: p. 18-23.
20.Amiruddin, A., et al. Secure multi-protocol gateway for Internet of Things. in 2018 Wireless Telecommunications Symposium (WTS). 2018.
21.Vargas, D.C.Y. and C.E.P. Salvador, Smart IoT Gateway For Heterogeneous Devices Interoperability. IEEE Latin America Transactions, 2016. 14(8): p. 3900-3906.
22.Al-Fuqaha, A., et al., Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 2015. 17(4): p. 2347-2376.
23.Shi, Y., et al. The fog computing service for healthcare. in 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech). 2015.
24.Aazam, M. and E. Huh, Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm. IEEE Potentials, 2016. 35(3): p. 40-44.
25.Rahmani, A., et al. Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems. in 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC). 2015.
26.Morabito, R., et al., LEGIoT: A Lightweight Edge Gateway for the Internet of Things. Future Generation Computer Systems, 2018. 81: p. 1-15.
27.Ren, J., et al., Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing. IEEE Network, 2017. 31(5): p. 96-105.
28.Mahmoud, M.M.E., et al., Towards energy-aware fog-enabled cloud of things for healthcare. Computers & Electrical Engineering, 2018. 67: p. 58-69.
29.Gia, T.N., et al. Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction. in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. 2015.
30.Fayos-Jordan, R., et al., Performance comparison of container orchestration platforms with low cost devices in the fog, assisting Internet of Things applications. Journal of Network and Computer Applications, 2020. 169: p. 1-13.
31.Luan, T.H.G., Longxiang, et al. Fog Computing: Focusing on Mobile Users at the Edge. 2015; https://ui.adsabs.harvard.edu/\#abs/2015arXiv150201815L.
32.Pan, J. and J. McElhannon, Future Edge Cloud and Edge Computing for Internet of Things Applications. IEEE Internet of Things Journal, 2018. 5(1): p. 439-449.
33.Adnan, N.A., et al., Study of generalized delay-timers in alarm configuration. Journal of Process Control, 2013. 23(3): p. 382-395.
34.Wang, J. and T. Chen, An online method to remove chattering and repeating alarms based on alarm durations and intervals. Computers & Chemical Engineering, 2014. 67: p. 43-52.
35.Zang, H., F. Yang, and D. Huang, Design and Analysis of Improved Alarm Delay-Timers. IFAC-PapersOnLine, 2015. 48(8): p. 669-674.
36.Noyes, J., Alarm systems: a guide to design, management and procurement. Computing & Control Engineering Journal, 2000. 11: p. 52-52.
37.Takai, T., Y. Kutsuma, and H. Ishihara, Management of Alarm Systems for the Process Industries. 2012. p. 688-692.
38.Freitas, L.L.G., et al., Analysis of water consumption in toilets employing Shewhart, EWMA, and Shewhart-EWMA combined control charts. Journal of Cleaner Production, 2019. 233: p. 1146-1157.
39.Assuncao, A.N., et al., Vehicle Driver Monitoring through the Statistical Process Control. Sensors, 2019. 19(14).
40.Aparisi, F. and J. Carlos Garcı́a-Dı́az, Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms. Computers & Operations Research, 2004. 31(9): p. 1437-1454.
41.Mukherjee, B., et al., Flexible IoT security middleware for end-to-end cloud–fog communication. Future Generation Computer Systems, 2018. 87: p. 688-703.
42.Sukparungsee, S., Y. Areepong, and R. Taboran, Exponentially weighted moving average—Moving average charts for monitoring the process mean. PLOS ONE, 2020. 15: p. e0228208.

電子全文 電子全文(網際網路公開日期:20250817)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊