The "Yoga Pose Correction using MoveNet" project focused on leveraging advanced computer vision techniques to enhance the practice of yoga. This innovative application utilized the MoveNet model, a state-of-the-art pose estimation framework, to accurately track and analyze human body positions in real-time. The system employed the user's camera to detect the position of various body parts and limbs, providing instant feedback on their alignment during yoga sessions. By training the model on a curated dataset of yoga poses, the application developed a robust understanding of ideal pose configurations.
The core functionality involved mapping specific key points on the body, such as joints and limb extremities, to create a virtual skeletal representation. These points were continuously monitored and compared against predefined standards for each yoga pose. If the user's pose aligned correctly with the model's expectations, the corresponding points on the screen would light up green, indicating proper execution. Conversely, misaligned points would appear in red, signaling areas requiring adjustment. This visual feedback mechanism allowed users to self-correct their posture in real-time, making the experience interactive and engaging.
A key aspect of the project was its focus on accessibility and usability. By integrating the MoveNet model, which is lightweight and optimized for performance, the application could run smoothly on standard devices without the need for specialized hardware. This design choice ensured that users could practice yoga effectively at home without the assistance of a professional instructor. The project also highlighted the importance of data preprocessing and augmentation, as diverse body types, camera angles, and lighting conditions were considered to make the model adaptable to real-world scenarios.
In summary, the "Yoga Pose Correction using MoveNet" project successfully demonstrated how artificial intelligence can revolutionize personal fitness by providing precise, real-time feedback on physical activity. By combining the power of pose estimation with intuitive visual cues, the project empowered users to refine their yoga practice, improve posture, and achieve better results.