Physical education teaching for saving energy in basketball sports athletics using Hidden Markov and Motion Model. (8th June 2020)

• Record Type: Journal Article
• Authors: Zhang, Ning; Han, Yubin; Crespo, Rubén G.; Martínez, Oscar S.
• Other Names: Srivastava Gautam guestEditor.; Hsu Ching‐Hsien (Robert) guestEditor.; Kumar Priyan Malarvizhi guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 3(2021)
• Page Start: 1125
• Page End: 1140
• Publication Date: 2020-06-08
• Subjects: basketball -- energy saving -- Hidden Markov model -- motion model -- physical education; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12343
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: A 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.