Zong-Jie Wu

As the epidemic affected everyone across the world, the solution to the epidemic was developed globally. Many applications adopt Internet of Things (IoT) technology to detect epidemics, and effective monitoring systems are developed to monitor air pollution, personal transmission, early detection of serious cases, and remote assessment. However, care facilities in an aging society require effective disinfection and sterilization to prevent viral transmission. We integrated the interactive and real-time features of the Internet of Things (IoT) to design and build an intelligent self-propelled sterilization robot for sterilization. Intelligent sterilization and disinfection planning and task allocation mechanisms were designed for sterilization in clinics. For healthcare facilities, the developed robot can reduce the burden on healthcare professionals, help to manage the disinfection and sterilization process, and ensure patient safety. At the same time, robots promote the development of epidemic prevention industries and prepares for future attacks from harmful air pollutants or new infections.

With the vigorous development of medical technology, the average human lifespan is increasing year by year. However, the probability of chronic diseases and physical degeneration is also growing proportionally. Among many chronic diseases, degenerative osteoarthritis is one of the common diseases among the elderly population. Depending on its severity, the worst cases require knee arthroplasty for treatment. After replacement surgery, patients need continuous rehabilitation exercises, follow-up, and observation. The rehabilitation process heavily relies on the guidance of professional physicians and the accuracy of the patient's rehabilitation movements. In order to improve the shortage of medical resources and the demand for continuous rehabilitation tracking, we propose a novel mobile technology-assisted remote rehabilitation detection and medical platform. It utilizes a three-axis accelerometer to measure the angle of knee joint rehabilitation. Through the rehabilitation guidance platform, patients are provided with prompts such as angles, frequencies, and movement content, effectively assisting patients in medical rehabilitation. Additionally, rehabilitation data collected through IoT devices during the patient's rehabilitation process are stored in a cloud-based medical platform. This platform allows physicians to analyze the activity data of patients' home rehabilitation behaviors and provide remote prescriptions, medical reminders, and patient Q&A to promote telemedicine. This facilitates real-time access to information for each patient, thereby improving the effectiveness of rehabilitation and realizing complementary added value.

Various levels of government across Taiwan are eager to promote the establishment of long-term care residential facilities to meet the significant caregiving needs arising from the wave of population aging. However, the successful establishment of an effective mechanism relies on proper supervision and guidance. Therefore, implementing a value assessment system for long-term care service quality management is of paramount importance. Using multi-criteria decision-making (MCDM) approach can provide effective conditions for the establishment of such system and enable a more comprehensive and objective evaluation of long-term care service quality. Using this system, decision-makers can incorporate different indicators based on various needs and weights to evaluate the quality and performance of long-term care services. This facilitates the determination of priorities and the formulation of improvement strategies, thereby enhancing the quality of long-term care services. This study develops an information-based assessment model for a platform that is win-win for both institutions and individuals. The model incorporates consumer reputation and environmental social governance (ESG) dimensions, in addition to indicators such as operational and management efficiency, professional care quality, safety and environmental facilities, and protection of individual rights and interests. Further, it integrates multiple indicator items and employs the analytic hierarchy process (AHP) to decompose and structure complex multi-dimensional issues, thereby aligning itself with current corporate evaluations. It aims to assist care service agencies in making key service quality decisions across different dimensions, and enhance the overall quality and competitiveness of those agencies, while increasing public trust and recognition in the evaluation of care service quality.

In the current long-term care environment, there is a shortage of manpower and a high turnover rate of staff. Therefore, residential institutions are eager to build an effective Internet of Things integration mechanism to assist institutions with automatic sensor detection and early warning capabilities. Although Internet of Things facilities have enabled prompt notification and warning of emergency events, the following problems exist when implementing Internet of Things in the facilities: (1) low compatibility between sensors has led to excessive installation costs; (2) warning systems that are based on fixed threshold values and lack of flexibility can cause false or omitted reports that result in the incapability of reflecting real conditions and additional labor costs would be required. This study uses a medical-grade Internet of Things module that can calculate the environmental values with edge computing to generate different levels of alarms by combining the index-weighted moving average method to dynamically calculate the optimal threshold value for the environment. It takes 2 months to collect data from care institutions. The average F1-Score obtained in different environments is between 0.46 and 0.88. The results show that compared with using a fixed threshold, this method can effectively reduce sensor error notifications and missed notifications.