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Physical education teaching for saving energy in basketball sports athletics using Hidden Markov and Motion Model

Authors: Rubén González Crespo; Rubén González Crespo; Yubin Han; Oscar Sanjuán Martínez; Ning Zhang;

Physical education teaching for saving energy in basketball sports athletics using Hidden Markov and Motion Model

Abstract

AbstractA new trend of schooling is characterized by long‐term learning and driven by technological, social, and cultural developments. This trend means that physical education (PE) and sports science must be strengthened. Although PE and sports are practical activities, specialists can make use of modern teaching technologies. Basketball is intended to develop the skills and understanding of movement and protection and its ability to take use of an active and healthy lifestyle in a variety of activities. Therefore, a survey suggested that, energy is valuable part in PE, especially more energy is increased by playing basketball. Hence this study concentrates on Hidden Markov hybridised with Motion Model (HM‐HMM), to save energy through the habit of playing basketball. The secret of HM‐HMM is computer evaluation system, particularly useful for the calculation of mastery of the academic knowledge of a collection of information points in pathways in PE in colleges to approximate and infer difficulties and unknown properties according to the observed variables. This article introduces a motion model which is more practical based on studies for player movement to save energy.

Keywords

Hidden Markov model, physical education, energy saving, JCR, Scopus, basketball, motion model

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    27
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
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