AIMC Topic: Athletes
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Enhancement of force patterns classification based on Gaussian distributions.
Journal of biomechanics
Dec 13, 2017
Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clin...
The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise.
Neural plasticity
Dec 27, 2015
The "neural efficiency" hypothesis suggests that experts are characterized by a more efficient cortical function in cognitive tests. Although this hypothesis has been extended to a variety of movement-related tasks within the last years, it is unclea...
Effects of pomegranate juice in circulating parameters, cytokines, and oxidative stress markers in endurance-based athletes: A randomized controlled trial.
Nutrition (Burbank, Los Angeles County, Calif.)
Dec 7, 2015
OBJECTIVE: The aim of the present study was to assess the effects of pomegranate juice on the level of oxidative stress in the blood of endurance-based athletes. Pomegranate juice is rich in polyphenols, conferring it a higher antioxidant capacity th...
Protein Carbonylation
Fruit and Vegetable Juices
Athletes
Physical Endurance
Stress, Physiological
Spain
Lythraceae
Malondialdehyde
Polyphenols
C-Reactive Protein
Double-Blind Method
Middle Aged
Oxidative Stress
Humans
E-Selectin
Antioxidants
Biomarkers
Sports Nutritional Physiological Phenomena
Adult
Male
Predictive Modeling in Race Walking.
Computational intelligence and neuroscience
Aug 3, 2015
This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers' training events and they are used to predict the result...
AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.
SLAS technology
Jun 1, 2025
The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Mode...
Classifying athletes and non-athletes by differences in spontaneous brain activity: a machine learning and fMRI study.
Brain structure & function
May 24, 2025
Different types of sports training can induce distinct changes in brain activity and function; however, it remains unclear if there are commonalities across various sports disciplines. Moreover, the relationship between these brain activity alteratio...
Predictive athlete performance modeling with machine learning and biometric data integration.
Scientific reports
May 11, 2025
The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate varia...
Predicting Sprint Potential: A Machine Learning Model Based on Blood Metabolite Profiles in Young Male Athletes.
European journal of sport science
Mar 1, 2025
This study aims to utilize male blood metabolite signatures for (i) distinguishing between healthy individuals and athletes, thereby optimizing the athlete screening process; and (ii) predicting athletic performance in 100, 200, and 400 m sprints, en...
Training forecast to football athletes using Hopfield neural networks based on Markov matrix.
PloS one
Jan 1, 2025
This paper proposes a neural network based on the Markov probability transition matrix to predict the training performance of football athletes. Firstly, seven training indicators affecting the training performance are designed by the Event-group tra...
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