Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition by Haitao Tian, Pierre Payeur

“…To overcome this limitation, the present work introduces an unsupervised temporal-domain adaptation framework for human action recognition from skeleton-based data that combines Contrastive Prototype Learning (CPL) and Temporal Adaptation Modeling (TAM), with the aim of transferring the knowledge learned from a source domain to an unlabeled target domain. …”
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Key Frame Detection in Badminton Swings and Its Application to Physical Education by Jen-Hao Hsu, Chi-Chuan Lee, Jing-Yuan Chang, Duan-Shin Lee

“…This model is integrated into a mobile app developed for Android tablets, allowing learners to record their swings and compare them with those of professional players, thereby enhancing badminton learning. Comparative studies show that models with graph convolutional networks, a prominent approach commonly used in skeleton-based human action recognition, outperform other existing methods in terms of accuracy and reliability. …”
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