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Author: 洪麒鈞
CHI-JIUN HUNG
Thesis Title: 應用於長期照護的照護員聲譽模型及模擬實驗
Caregiver Reputation Model for Long-term Care and its Simulation Experiments
Advisor: 羅乃維
Nai-Wei Lo
Committee: 楊傳凱
Chuan-Kai Yang
查士朝
Shi-Cho Cha
Degree: 碩士
Master
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2019
Graduation Academic Year: 107
Language: 英文
Pages: 47
Keywords (in Chinese): 物聯網聲譽時間滑動視窗
Keywords (in other languages): Internet of Things, Reputation, Time Sliding Window
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  • 隨著全球人口高齡化以及老年人得到慢性病的比例逐漸上升,老人的長期照護需求正在不斷增加。因此越來越多的照護員經由雇傭進入家庭之中,透過照護員的服務讓家庭的照護壓力得以緩解。但是由於家庭成員不了解照護員過去的服務紀錄,因此可能雇傭水準較差的照護員但卻在短時間內無法發現。故需要有一套機制讓家庭成員能夠監控照護員的服務以此確認家庭中的老人有被妥善照護。

    傳統的解決方案是使用IP攝影機對照護員進行監控,但家庭成員通常不會頻繁地這麼做,這導致照護員的失職可能不會被發現。因此基於物聯網框架之照護員聲譽模型是必須的。物聯網裝置已經進入了人們的生活之中,無所不在的物聯網裝置無疑是監控照護員服務狀況的最佳工具。照護員聲譽模型可以利用物聯網裝置感測的資料計算照護員的聲譽,讓家庭成員可以藉此了解照護員的服務狀況,並且可以根據照護員的聲譽分數採取行動,像是和照護員討論服務不良的原因以及如何改善。

    綜合上述,本研究設計了一個基於物聯網框架之照護員聲譽模型,藉由物聯網裝置感測的照護員服務資料,照護員聲譽模型可以計算照護員的聲譽以代表他的服務品質。另外,利用時間滑動視窗的概念,將照護員過去的服務紀錄加入模型的計算之中以降低照護員聲譽的波動,並且將過去服務狀況的影響力延伸到未來。照護員每天的服務狀況以及聲譽都會被儲存於閘道器以及雲端,故家庭成員可以藉由這些資訊了解照護員的服務狀況。最後,本研究亦對所設計的模型進行模擬實驗以及有效性評估,以確保模型計算出的聲譽可以代表照護員的服務品質。


    As the proportion of the elderly in the population increases and the proportion of elderly people getting chronic diseases increases, the long-term care needs of the elderly are increasing. Therefore, more and more caregivers enter the family through employment, but because family members do not know the caregiver's past service records, they may hire disappointing caregivers but they will not be able to discover this fact in short time. Therefore, a mechanism is needed to enable family members to monitor the caregiver's services to confirm that the elderly in the family are properly cared for.

    The traditional solution is to use an IP camera to monitor the caregiver, but family members usually do not do this frequently, which may result in the caregiver's poor service not to be discovered. Therefore, a caregiver reputation model based on the IoT framework is necessary. IoT devices have entered people's lives, and the ubiquitous IoT device is undoubtedly the best tool for monitoring the caregiver's service status. The caregiver reputation model can use the data sensed by the IoT device to calculate the reputation of the caregiver so that family members can identify the caregiver's service status and take action based on the caregiver's reputation. It is like discussing with the caregiver the reasons for poor service and how to improve.

    Therefore, this thesis designs a caregiver reputation model based on the Internet of Things framework. The caregiver reputation model can be used to calculate the reputation of the caregiver to represent caregiver's service quality through the service data of the caregiver sensed by the IoT device. In addition, using the concept of Time Sliding Window, the caregiver's past service records are added to the model's calculations to reduce the volatility of the caregiver's reputation and extend the influence of past services to the future. The daily service status and reputation of the caregiver are stored in the gateway and the cloud, so family members can use those information to understand the service quality of the caregiver. Finally, the thesis also carried out simulation experiments and effectiveness assessments on the designed models to ensure that the reputation calculated by the model can represent the quality of service of the caregiver.

    摘要 I Abstract II 誌謝 III List of Contents IV List of Figures VI List of Tables VII Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Goal 4 Chapter 2. Related Work 6 2.1 Reputation Model 6 2.2 Monitoring System for Internet of Things 8 Chapter 3. Caregiver Reputation Model 12 3.1 Scenario Environment 12 3.2 Services in the Caregiver Reputation Model 14 3.3 Symbol Definition 17 3.4 Reputation Model 18 Chapter 4. Model Evaluation and Simulation Experiments 24 4.1 Analysis of the Experiment Results 25 4.2 Effectiveness Evaluation for the Caregiver Reputation Model 28 Chapter 5. Conclusion 32 References 35

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