<![CDATA[Monitoring hand muscle movements in stroke patients is a crucial aspect of the rehabilitation process to track therapy progress. However, manual monitoring by medical personnel requires significant time and effort. To address this issue, this study aims to design and develop a hand muscle movement monitoring system based on the Internet of Things (IoT). The system is designed to monitor finger movements using flex sensors attached to a glove, with data transmitted wirelessly to a web-based monitoring platform. The methodology used in this research involves the development of both hardware and software to record and transmit finger movement data in real-time. The system is also equipped with notifications via buzzers and LEDs to assist patients in adhering to therapy schedules set by medical professionals. Testing was conducted on stroke patients and healthy individuals to measure the system's effectiveness. The results indicate that this system can accurately and consistently monitor hand muscle movements in stroke patients. Trials conducted over five consecutive days on stroke patients showed that the average finger measurements ranged from 9.11 to 9.97, with significant differences in finger strength compared to healthy individuals. These findings suggest that this monitoring system is effective in supporting the rehabilitation process of stroke patients, enabling medical personnel to monitor and evaluate patient progress more accurately.]]>
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