Abstract
This research explores the intricacies of volleyball action recognition using skeleton data through the lens of the Long Short-Term Memory (LSTM) model. With the objective of accurately identifying distinct volleyball actions—Serve, Spike, Block, Dig, and Set—the study implemented a structured LSTM network, achieving a commendable 95% accuracy rate consistently across all actions. The findings underscore the transformative potential of deep learning, particularly the LSTM network, in sports analytics, suggesting a paradigm shift in understanding and analyzing sports actions. The research serves as a foundation for future studies, offering insights into the blend of artificial intelligence in sports, with applications extending to coaching support and enhanced sports broadcasts.
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