Restricted Hidden Markov Model based Innovation in Physical Learning Education with Computer Internet Technology
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Abstract
The integration of computer internet technology into college physical education teaching has greatly enriched the teaching methods and forms and has promoted the innovation of the physical education teaching mode. The integration of computer internet technology into college physical education teaching mode has brought about significant innovation and improvement, enabling teachers to provide richer, more interactive, personalized, and flexible teaching. The integration of computer internet technology in physical education teaching has opened up new opportunities for enhancing the learning experience and improving student outcomes. This research focuses on the development of a technological innovation that leverages deep learning techniques, specifically the Restricted Hidden Markov Model (RHMMDL), to optimize physical education instruction. By harnessing the power of deep learning algorithms, the RHMMDL model aims to analyze and interpret movement patterns, providing personalized feedback and guidance to students. This paper presents the conceptual framework and methodology of the RHMMDL model, highlighting its potential benefits and implications for physical education teaching. The study also discusses the challenges and limitations associated with the implementation of this innovative technology.
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