AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Yoga represents an age-old practice that is beneficial for both psychological and physical health. The yoga promotes self-learning and improper poses can seriously harm muscles and ligaments. The accurate recognition of yoga poses from images remains challenging due to high intra-class similarity, background variations...
This study aims to develop a human body posture classification model based on digital images using the InceptionV3 architecture. The dataset used in this study is the MPII Human Pose Dataset, which contains a wide variety of human activities and body postures. The research began with label extraction from a metadata fi...
With the development of artificial intelligence and deep learning technology, human pose estimation has been widely applied in fields such as medical rehabilitation and motion analysis. To meet the needs of objective assessment of lower limb function in patients with knee joint diseases, we propose a method for detecti...
Human posture recognition is important in sports training because it allows proper evaluation of the technique of an athlete, enhances performance, and reduces the risk of injury. Conventional techniques, including coach manual evaluation and motion capture systems, are generally timeconsuming, expensive, and not scala...
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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