4 research outputs found

    The role of football video games in boosting cognitive abilities critical to football performance

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    Background and Aim: Football requires rapid decision-making and quick reaction times, crucial cognitive abilities that traditionally develop through physical training. This study explores whether football video games, which simulate real game scenarios, can enhance these cognitive abilities among college-level male football players. Materials and Methods: The study involved 51 football players from SRM Group of Institutions, divided into three groups: regular video game players (Group A), occasional players (Group B), and non-players (Group C). A mixed-methods approach was used, combining quantitative cognitive abilities tests (decision-making, reaction time, situational awareness). The video game used in the study was eFootball 2024 with participants engaging in gameplay over a period of 6 weeks. Tests were conducted before and after a set period of video game engagement. Data were analyzed using mixed-design ANOVA and Pearson’s correlation. Results: Significant improvements were observed in Group A across all cognitive abilities tested. Decision-making showed notable group and time effects (F (2, 48) = 5.76, p = 0.005, η² = 0.17; F (1, 48) = 12.54, p < 0.001, η² = 0.25), with interaction effects indicating substantial enhancements over time (F (2, 48) = 3.45, p = 0.035, η² = 0.08). Reaction time and situational awareness followed similar patterns, with significant group, time, and interaction effects. Correlation analysis revealed strong interrelations between the cognitive abilities, indicating that improvements in one area positively influenced others. Conclusion: Regular engagement with football video games significantly enhances cognitive abilities essential for football performance. These findings suggest that integrating video games into training programs could complement traditional methods, offering a valuable tool for cognitive development in sports

    The role of football video games in boosting cognitive abilities critical to football performance

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    Background and Aim:Football requires rapid decision-making and quick reaction times, crucial cognitive abilitiesthat traditionally develop through physical training. This study explores whether football video games, which simulate real game scenarios, can enhance these cognitive abilities among college-level male football players.Materials and Methods:The study involved 51 football players from SRM Group of Institutions, divided into three groups: regular video game players (Group A), occasional players (Group B), and non-players (Group C). A mixed-methods approach was used, combining quantitative cognitive abilitiestests (decision-making, reaction time, situational awareness). The video game used in the study was eFootball 2024 with participants engaging in gameplay over a period of 6 weeks. Tests were conducted before and after a set period of video game engagement. Data were analyzed using mixed-design ANOVA and Pearson’s correlation.Results:Significant improvements were observed in Group A across all cognitive abilitiestested. Decision-making showed notable group and time effects (F (2, 48) = 5.76, p = 0.005, η²= 0.17; F (1, 48) = 12.54, p < 0.001, η²= 0.25), with interaction effects indicating substantial enhancements over time (F (2, 48) = 3.45, p = 0.035, η²= 0.08). Reaction time and situational awareness followed similar patterns, with significant group, time, and interaction effects. Corre-lation analysis revealed strong interrelations between the cognitive abilities, indicating that improvements in one area positively influ-enced others.Conclusion:Regular engagement with football video games significantly enhances cognitive abilitiesessential for football performance. These findings suggest that integrating video games into training programs could complement traditional methods,of-fering a valuable tool for cognitive development in sports.Antecedentes y objetivo: El fútbol requiere una toma de decisiones rápida y tiempos de reacción rápidos, capacidades cognitivas cruciales que tradicionalmente se desarrollan mediante el entrenamiento físico. Este estudio explora si los videojuegos de fútbol, que simulan escenarios de juego reales, pueden mejorar estas capacidades cognitivas entre los jugadores de fútbol masculino de nivel universitario. Materiales y métodos: En el estudio participaron 51 jugadores de fútbol del Grupo de Instituciones SRM, divididos en tres grupos: jugadores habituales de videojuegos (Grupo A), jugadores ocasionales (Grupo B) y no jugadores (Grupo C). Se utilizó un enfoque de métodos mixtos, combinando pruebas cuantitativas de habilidades cognitivas (toma de decisiones, tiempo de reacción, conciencia situacional). El videojuego utilizado en el estudio fue eFootball 2024 y los participantes jugaron durante 6 semanas. Las pruebas se realizaron antes y después de un periodo determinado de participación en el videojuego. Los datos se analizaron mediante ANOVA de diseño mixto y correlación de Pearson. Resultados: Se observaron mejoras significativas en el grupo A en todas las capacidades cognitivas evaluadas. La toma de decisiones mostró notables efectos de grupo y tiempo (F (2, 48)= 5,76, p = 0,005, η²= 0,17; F (1, 48) = 12,54, p < 0,001, η²= 0,25), con efectos de interacción que indicaban mejoras sustanciales con el tiempo (F (2, 48) = 3,45, p = 0,035, η²= 0,08). El tiempo de reacción y la conciencia situacional siguieron patrones similares, con efectos significativos de grupo, tiempo e interacción. El análisis de correlación reveló fuertes interrelaciones entre las capacidades cognitivas, indicando que las mejoras en un área influían positivamente en las demás. Conclusiones: La participación regular en videojuegos de fútbol mejora significativamente las capacidades cognitivas esenciales para el rendimiento futbolístico. Estos resultados sugieren que la integración de los videojuegos en los programas de entrenamiento podría complementar los métodos tradicionales, ofreciendo una valiosa herramienta para el desarrollo cognitivo en el deporte

    Large‐scale system identification using self‐adaptive penguin search algorithm

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    Abstract From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches
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