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      The Application of Human–Computer Interaction Technology Fused With Artificial Intelligence in Sports Moving Target Detection Education for College Athlete

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          Abstract

          The purposes are to digitalize and intellectualize current professional sports training and enrich the application scenarios of motion capture technology of moving targets based on artificial intelligence (AI) and human–computer interaction (HCI) in sports training. From an educational psychology perspective, sport techniques are a cognitive ability of sports, and a tacit knowledge. However, sports technology, language, image, and other methods play an auxiliary role in sports training. Here, a General Framework of Knowledge-Based Coaching System (KBCS) is proposed using the HCI technology and sports knowledge to accomplish autonomous and intelligent sports training. Then, the KBCS is applied to table tennis training. The athletic performance is evaluated quantitatively through the calculation of the sports features, motion recognition, and the hitting stage division in table tennis. Results demonstrate that the speed calculated by the position after mosaicking has better continuity after the initial frame of the unmarked segment is compared with the end frame of the market segment. The typical serve and return trajectories in three serving modes of slight-spin, top-spin, and back-spin, as well as the trajectories of common services and return errors, are obtained through the judgment of the serving and receiving of table tennis. Comparison results prove that the serving accuracy of slight-spin and back-spin is better than that of top-spin, and a lower serve speed has higher accuracy. Experimental results show that the level distribution of the three participants calculated by the system is consistent with the actual situation in terms of the quality of the ball returned and the standard of the motion, proving that the proposed KBCS and algorithm are useful in a small sample, thereby further improving the accuracy of pose restoration of athletes in sports training.

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          Most cited references34

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          Patient self-management of chronic disease in primary care.

          Patients with chronic conditions make day-to-day decisions about--self-manage--their illnesses. This reality introduces a new chronic disease paradigm: the patient-professional partnership, involving collaborative care and self-management education. Self-management education complements traditional patient education in supporting patients to live the best possible quality of life with their chronic condition. Whereas traditional patient education offers information and technical skills, self-management education teaches problem-solving skills. A central concept in self-management is self-efficacy--confidence to carry out a behavior necessary to reach a desired goal. Self-efficacy is enhanced when patients succeed in solving patient-identified problems. Evidence from controlled clinical trials suggests that (1) programs teaching self-management skills are more effective than information-only patient education in improving clinical outcomes; (2) in some circumstances, self-management education improves outcomes and can reduce costs for arthritis and probably for adult asthma patients; and (3) in initial studies, a self-management education program bringing together patients with a variety of chronic conditions may improve outcomes and reduce costs. Self-management education for chronic illness may soon become an integral part of high-quality primary care.
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            Flexible and durable wood-based triboelectric nanogenerators for self-powered sensing in athletic big data analytics

            In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.
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              Do perceived autonomy-supportive and controlling teaching relate to physical education students' motivational experiences through unique pathways? Distinguishing between the bright and dark side of motivation

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                22 July 2021
                2021
                : 12
                : 677590
                Affiliations
                [1] 1Sports Training and Health Care, Zhoukou Normal University , Zhoukou, China
                [2] 2Sports Training, Zhongyuan University of Technology , Zhengzhou, China
                [3] 3Computer Science and Technology, Zhoukou Normal University , Zhoukou, China
                Author notes

                Edited by: Yaodong Gu, Ningbo University, China

                Reviewed by: Yang Song, Óbuda University, Hungary; Xiaoxue Zhao, Lodz University of Technology, Poland

                *Correspondence: Jie Liu liujie515@ 123456126.com

                This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2021.677590
                8339562
                34366996
                6ba9321e-b87d-418d-869c-aaafec8d6c86
                Copyright © 2021 Liu, Wang and Zhou.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 March 2021
                : 18 June 2021
                Page count
                Figures: 12, Tables: 1, Equations: 11, References: 34, Pages: 12, Words: 7326
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                artificial intelligence,framework of coaching system,motion capture technology,human-computer interaction,sports training

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