Published Paper Details:

This project presents a novel approach to game control using computer vision techniques for pose detection. By leveraging machine learning algorithms, specifically pose estimation models, the system interprets the user's body movements to generate control commands for games. The proposed system aims to provide intuitive and immersive gaming experiences by mapping common game actions such as 'up', 'down', 'left', and 'right' to corresponding detected poses. Unlike traditional input devices, this method offers greater freedom of movement, potentially enhancing accessibility for players with disabilities or limited mobility. The versatility of the system allows integration with various game genres, providing a seamless and adaptable control mechanism across different gaming platforms. Experimental results demonstrate the feasibility and effectiveness of the proposed approach, showcasing its potential to revolutionize game interaction through natural body gestures. The versatility of our system is noteworthy as it seamlessly integrates with a wide range of game genres, offering a flexible and adaptable control mechanism across various gaming platforms. Through rigorous experimentation and testing, we demonstrate the feasibility and efficacy of our proposed approach, showcasing its potential to redefine game interaction through natural body gestures. Our findings suggest that this innovation has the capability to revolutionize the gaming landscape by bridging the gap between physical movement and virtual gameplay, unlocking new avenues for immersive gaming experiences.