SISTEM MONITORING CHINNING UP DAN PULL UP BERBASIS INTERNET OF THINGS
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Abstract
This study was conducted to design and develop an automated counting system for Chinning Up and Pull Up exercises based on the Internet of Things, aimed at improving the accuracy and efficiency of physical fitness assessment. The proposed system integrates a SHARP GP2Y0A21 infrared sensor to detect movement repetitions and an electromyography sensor to measure biceps and triceps activity. All captured data are processed by an ESP32 microcontroller and transmitted directly to Firebase Realtime Database, then displayed through a web interface developed using Laravel. The system supports three user roles—admin, supervisor, and athlete—each responsible for account management, training monitoring, and access to performance records. Testing procedures were carried out using Blackbox Testing and User Acceptance Test to evaluate measurement accuracy and usability. The results indicate that the system can identify repetitions with an accuracy of 96.8% and measure muscle activity with an accuracy of 94.5%. Training data are presented in real time and stored as monitoring records. Based on these findings, the system is considered effective in providing an objective, measurable, and integrated evaluation mechanism for exercise performance
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References
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