TL;DR
This study develops a skeleton-imposed images-based CNN model, contrasting traditional CNN approaches that typically rely solely on body shape or key point representations, that enhances the accuracy of yoga pose classification, providing a more nuanced understanding of complex poses.
An integrated multi-layer hybrid framework for accurate, real-time posture assessment in healthcare and rehabilitation contexts is proposed, although all solutions trade off accuracy, computational cost, and practical generalizability.
A model that combined the MobileNet with Hunter-Prey Optimization, and Long Short-Term Memory to detect the efficient and accurate yoga poses and enhances classification performance and maintain the efficiency of model is proposed.
A human body posture classification model based on digital images using the InceptionV3 architecture was developed using a transfer learning approach with InceptionV3 as the base model, indicating that the model was able to classify most activity classes effectively.
The correlation between the visual algorithm output and clinical functional status was further evaluated using established clinical indicators, including the Knee injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Timed Up and Go (TUG) test, and the 3...
This method is of great benefit compared to conventional techniques by providing a computationally cost-effective, scalable, and highly accurate posture recognition system, which ultimately leads to improved sports training results and the minimization of injury risks.
The proposed Media Pipe–CNN framework provides an efficient, accurate, and marker less solution for automated ergonomic risk assessment, supporting intelligent occupational safety management, continuous workplace monitoring, and the implementation of smart manufacturing systems aligned with Industry 4.0 initiatives.
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