Real-Time Hand Gesture Recognition for Touchless Video Control Using MediaPipe and Random Forest

This work presents a method for interacting with video players through real-time hand gesture recognition, implemented using the MediaPipe library and the Random Forest algorithm. The main goal was to develop a lightweight and accessible system capable of controlling playback, time navigation, and volume adjustment without requiring physical contact with the screen. The prototype was developed for Android devices and evaluated through an experiment conducted under real usage conditions, where users interacted with the system on their own mobile devices. Usability was assessed using the System Usability Scale (SUS), which resulted in an average score of 83.38, classified as "Good." The user experience was further evaluated using the short version of the User Experience Questionnaire (UEQ-S), which yielded "Good" results in the pragmatic dimension and "Excellent" in the hedonic dimension. The results indicate that the system is intuitive, functional, and pleasant to use, even among users with little prior experience with gesture-based interaction. The model demonstrated good generalization capabilities, even with a reduced training dataset. The proposed approach proved to be viable and promising for future applications in various contexts, such as entertainment, education, and accessibility.