Acceso usuarios
City of Mandaluyong, Filipinas
Debido a las características especiales de los deportes competitivos, los atletas de alto nivel son más propensos a problemas psicológicos ya que sus cuerpos mentales y físicos están sujetos a una presión psicológica a largo plazo y desafíos de estimulación de alta intensidad que son difíciles de experimentar para la gente común. La aparición de estos problemas afectará directamente el nivel y el potencial competitivo de los atletas, e incluso puede terminar sus carreras deportivas de forma prematura, causándoles daños irreparables. Este documento propone un enfoque de aprendizaje automático basado en datos para la prevención de lesiones y el apoyo a la toma de decisiones sobre el tratamiento. Las ventajas del método de aprendizaje automático radican principalmente en lo siguiente: en primer lugar, todo el proceso de aprendizaje automático, desde la selección de características hasta el modelado analítico y la predicción, está estrechamente centrado en los datos y no se verá interferido por el conocimiento a priori, por lo que puede extraer eficazmente los factores de influencia desatendidos que son difíciles de encontrar en el método tradicional, de modo que el proceso de selección de características se pueda completar con mayor precisión y pueda respaldar eficazmente la gestión científica de la salud del atleta.
Due to the special characteristics of competitive sports, high-level athletes are more prone to psychological problems as their mental and physical bodies are subjected to long-term psychological pressure and high-intensity stimulation challenges that are difficult for ordinary people to experience. The emergence of these problems will directly affect the athletes' competitive level and potential, and may even end their athletic careers prematurely, causing irreparable damage to themselves. This paper proposes a data-driven machine learning approach for injury prevention and treatment decision support. The advantages of the machine learning method mainly lie in the following: firstly, the whole process of machine learning, from feature selection to analytical modelling and up to prediction, is tightly focused on the data, and will not be interfered by the a priori knowledge, so it can effectively extract the unattended influencing factors which are difficult to be found in the traditional method, so that the feature selection process can be more accurately completed, and it can effectively support the scientific athlete's health Due to the special characteristics of competitive sports, high-level athletes are more prone to psychological problems as their mental and physical bodies are subjected to long-term psychological pressure and high-intensity stimulation challenges that are difficult for ordinary people to experience. The emergence of these problems will directly affect the athletes' competitive level and potential, and may even end their athletic careers prematurely, causing irreparable damage to themselves. This paper proposes a data-driven machine learning approach for injury prevention and treatment decision support. The advantages of the machine learning method mainly lie in the following: firstly, the whole process of machine learning, from feature selection to analytical modelling and up to prediction, is tightly focused on the data, and will not be interfered by the a priori knowledge, so it can effectively extract the unattended influencing factors which are difficult to be found in the traditional method, so that the feature selection process can be more accurately completed, and it can effectively support the scientific athlete's health management. KEYWORDS:Athlete Health Management; Data-driven Decision Support; Injury Prevention; Injury Treatmenmanagement. KEYWORDS:Athlete Health Management; Data-driven Decision Support; Injury Prevention; Injury TreatmenDue to the special characteristics of competitive sports, high-level athletes are more prone to psychological problems as their mental and physical bodies are subjected to long-term psychological pressure and high-intensity stimulation challenges that are difficult for ordinary people to experience. The emergence of these problems will directly affect the athletes' competitive level and potential, and may even end their athletic careers prematurely, causing irreparable damage to themselves. This paper proposes a data-driven machine learning approach for injury prevention and treatment decision support. The advantages of the machine learning method mainly lie in the following: firstly, the whole process of machine learning, from feature selection to analytical modelling and up to prediction, is tightly focused on the data, and will not be interfered by the a priori knowledge, so it can effectively extract the unattended influencing factors which are difficult to be found in the traditional method, so that the feature selection process can be more accurately completed, and it can effectively support the scientific athlete's health management.
