![]() Integrative training for children and adolescents: techniques and practices for reducing sports-related injuries and enhancing athletic performance. ![]() Myer GD, Faigenbaum AD, Chu DA, Falkel J, Ford KR, Best TM, Hewett TE. Body composition, dietary intake and the risk of low energy availability in elite-level competitive rhythmic gymnasts. Villa M, Villa-Vicente JG, Seco-Calvo J, Mielgo-Ayuso J, Collado P. Competitive state anxiety and performance in young female rhythmic gymnasts. Analysis of the training load during the competitive period in individual rhythmic gymnastics. Fact Univ Ser Phys Educ Sport 2016 14:63–74.įernandez-Villarino MA, Sierra-Palmeiro E, Bobo-Arce M, Lago-Peñas C. ![]() The importance of motor coordination abilities for performance in rhythmic gymnastics. Purenović-Ivanović TM, Popović R, Stanković D, Bubanj S. Physiological and anthropometric determinants of rhythmic gymnastics performance. J Hum Sport Exerc 2013 8:711–27.ĭouda HT, Toubekis AG, Avloniti AA, Tokmakidis SP. Determinants of competitive performance in rhythmic gymnastics: a review. Qual Res Sport Exerc Health 2017 9(5):533–45.īobo-Arce M, Méndez Rial B. Adjusting to retirement from sport: Narratives of former competitive rhythmic gymnasts. Theory Pract Phys Cul 2021 10:69–71.Ĭavallerio F, Wadey R, Wagstaff CR. Sports science integration for progress of gymnastics disciplines. Sports training teaching device based on big data and cloud computing. Powered by intelligent fabric, the proposed advanced performance analysis system exhibits the potential to promote innovative technologies for improving training and competitive performance, prolonging athletic careers, as well as reducing sports injuries. In addition, four typical applications are presented to improve training performance. A feasible solution to implementing the analysis component is the use of the hyperdimensional computing technique. After a variety of data are collected, the analysis component is implemented by artificial intelligence techniques resulting in behavior recognition, decision-making, and other functions assisting performance improvement. The system uses intelligent fabric to detect the physiological and anthropometric data of the gymnasts. Thus, an advanced performance analysis system for rhythmic gymnastics is proposed in this paper, powered by intelligent fabric. However, there are three primary limitations of traditional performance analysis systems applied in rhythmic gymnastics: (1) Inability to quantify anthropometric data in an imperceptible way, (2) labor-intensive nature of data labeling and analysis, and (3) lack of monitoring of all-round and multi-dimensional perspectives of the target. To have an accurate insight about the motion and postures can help the optimization of their performance and offer personalized suggestions. Performance analysis is an important tool for gymnasts and coaches to assess the techniques, strengths, and weaknesses of rhythmic gymnasts during training.
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