<![CDATA[General Background: Stroke remains a major health problem, and continuous monitoring of motor function is important for assessing patient rehabilitation progress. Specific Background: Hand grip strength is commonly used to evaluate motor conditions in post-stroke patients; however, previous measurement systems either required manual patient data recording or relied on limited-capacity local storage. Knowledge Gap: Existing hand grip measurement devices have not adequately provided integrated patient identification and cloud-based data storage for long-term monitoring. Aims: This study aimed to develop a hand grip strength measurement system for stroke patients using Internet of Things (IoT) technology, RFID-based patient identification, and Google Spreadsheet cloud storage. Results: The proposed system utilized an ESP32 module, RFID-RC522, load cell sensor, HX711 module, and LCD display. Testing showed a load cell error rate of 0.3%, an average RFID data transmission delay of 3.4 seconds, and an overall system error of 1.09% based on measurements from 10 stroke patients compared with a CAMRY Hand Dynamometer Model 101. Measurement results were automatically stored in Google Spreadsheet and could be accessed by healthcare providers and patient families. Novelty: The system integrates hand grip assessment, RFID-based patient identification through E-KTP, and cloud-based data storage within a single IoT platform. Implications: This design provides a practical approach for structured and accessible monitoring of hand grip strength data in stroke patient therapy programs.Highlights: Sensor calibration testing produced a measurement error of only 0.3%. RFID-RC522 transmitted patient identification data with an average delay of 3.4 seconds. Comparison with a commercial dynamometer showed a mean system error of 1.09% across ten participants. Keywords: Stroke; Hand Grip Strength; Internet of Things; Google Spreadsheet; RFID]]>
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