INTERNET OF THINGS–BASED CHINNING UP AND PULL UP COUNTING SYSTEM USING INFRARED AND EMG SENSORS
Abstract
This study aims to design and implement an automatic counting tool for Chinning Up and Pull Up exercises based on the Internet of Things (IoT) to improve accuracy and efficiency in physical fitness assessment. The system uses SHARP GP2Y0A21 infrared sensors to detect the number of repetitions of the movement and Electromyography (EMG) sensors to measure biceps and triceps muscle activity during exercise. Data from both sensors is processed by an ESP32 microcontroller and sent in real-time to the Firebase Realtime Database, then displayed through a web interface based on the Laravel Framework. This system consists of three user roles, namely admin, supervisor, and athlete, each with different management, monitoring, and exercise result access functions. Testing was conducted using Blackbox Testing and User Acceptance Test (UAT) to assess system performance and user experience. The test results showed that the system had a sensor reading accuracy rate of 96.8% for infrared sensors and 94.5% for EMG sensors in detecting real-time muscle movements and activities. The measurement results can be accessed directly and responsively through the website, facilitating the process of monitoring athlete performance. Thus, this system has proven to be effective in supporting objective, efficient, and integrated physical training evaluation.
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