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telemedicine
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Simulation-Based Optimization for IoT-Enabled Epidemic Patients Care Systems
,
M.Sc. Thesis
Sharif University of Technology
;
Hassan Nayebi, Erfan
(Supervisor)
Abstract
This research focuses on examining and improving healthcare systems, particularly during crises and pandemics. Following disasters such as natural calamities and pandemics like COVID-19, healthcare systems face significant challenges due to the increased demand for medical services, creating a substantial threat to the population in the affected regions. This study emphasizes the importance of utilizing modern technologies such as the Internet of Things (IoT) and telemedicine systems in alleviating the pressure on healthcare systems. A combined approach of prediction and multi-objective optimization based on simulation is proposed in this study to improve resource allocation and demand...
Identifying the Influencing Factors of mHealth Technology Adoption in Iran
, M.Sc. Thesis Sharif University of Technology ; Isaai, Mohammad Taghi (Supervisor)
Abstract
This study investigates the factors influencing the acceptance of digital health technologies in Iran by employing a modified version of the Unified Theory of Acceptance and Use of Technology (UTAUT2). The significance of understanding user acceptance is underscored within the context of digital health ecosystems, which consist of various stakeholders, including healthcare providers, technology companies, and patients. These platforms serve as vital interfaces that connect these stakeholders, facilitating access to healthcare services and enhancing health outcomes. The literature review reveals that while traditional models like TAM and UTAUT2 have been widely applied, they often overlook...
Implementing a Visual Assistant for Elderly Care using CCTV Camera
, M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Naejesolhoda (Supervisor)
Abstract
In this research, an intelligent and integrated system for elderly fall detection monitoring based on CCTV cameras has been designed and implemented. The system consists of three main components: a fall detection device, a central server, and a mobile application. The fall detection algorithm runs in real time on a Raspberry Pi 4B board and combines classical methods with deep learning techniques. Specifically, an SVM model is employed in the classical stage, while Convolutional Neural Networks (CNN) and MoveNet models are utilized in the deep learning stage to enhance accuracy. To ensure security and privacy, the system incorporates end-to-end encryption and secure HTTPS-based...