Real-time yoga posture correction using deep learning for individuals with physical disabilities

The rapidly increasing global adoption of yogic practice as a comprehensive inclination towards wellness has significantly increased the need for advanced as well as effective methods to recognize yoga postures. This study investigates a solution based on deep learning to deliver real-time yoga pose correction, tailored specifically for individuals with physical disabilities. For many, access to a yoga instructor or personal guide is not feasible, making artificial intelligence a practical alternative for offering pose guidance from home. Existing research on pose estimation in yoga primarily focuses on classifying poses for people without physical impairments. In our novel approach, we use the Thunder variant of the ultra fast MoveNet model, which enables real-time pose estimation, to identify and correct posture errors for individuals with lower-body disabilities. The proposed system applies deep convolutional neural networks (CNNs) to evaluate user posture through video inputs. This study enhances accessibility in chair yoga practices for people with physical challenges by providing accurate real-time pose corrections using deep learning methods, and by curating a state-of-the-art dataset for chair yoga poses, not worked upon before.