| dc.description.abstract | Taiwan is already an aged society, and the National Development Council (NDC) estimates that it will become a super-aged society by 2025, which will increase the demand for healthcare. However, in recent years, hospitals have faced a shortage of registered professional nurses and pharmacists. In addition to the high-pressure work environment, high working hours and low pay, the high turnover rate of hospital pharmacists is due to the need to work night shifts and to handle a large number of patients′ dispensing duties. The shortage of manpower has led to long queues at the medicine collection windows of many hospitals, and the increase in waiting time for medicine collection has also had a negative impact on the patients′ experience. This thesis proposes an edge computing-based intelligent medicine collection system, which aims to operate ‘side-by-side’ with the traditional National Health Insurance (NHI) card to provide an alternative way to collect medicines, thus effectively reducing the pressure and waiting time of medicine collection and improving the efficiency of medicine adoption. The system utilizes the latest generation Raspberry Pi 5 as an edge device, and combines YOLOv5s and FaceNet to achieve real-time face recognition authentication, ensuring that patients receive their medication correctly. With the concept of edge computing, the process of face detection and recognition is done entirely on the Raspberry Pi at the edge, eliminating the need to transfer the data to a cloud server for processing, thus speeding up the data processing. Finally, the model and related data are stored in the AWS cloud service, and a LINE Bot is created to allow double authentication of the patient, further enhancing the security of the system. This thesis is also expected to provide novel application concepts for the realization of human-less pharmacy in the future. | en_US |