AI Medical Compendium Journal:
European journal of sport science

Showing 1 to 8 of 8 articles

Predicting special forces dropout via explainable machine learning.

European journal of sport science
Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or no...

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests.

European journal of sport science
The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspect...

Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data.

European journal of sport science
This study examined a series of machine learning models, evaluating their effectiveness in assessing children's energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also de...

The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers.

European journal of sport science
The use of rolling averages to analyse training data has been debated recently. We evaluated two training load quantification methods (five-zone, seven-zone) fitted to performances over two race distances (50 and 100 m) using four separate longitudin...

Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.

European journal of sport science
First and second ventilatory thresholds (VT and VT) represent the boundaries of the moderate-heavy and heavy-severe exercise intensity. Currently, VTs are primarily detected visually from cardiopulmonary exercise test (CPET) data, beginning with an i...

Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks.

European journal of sport science
The soaring amount of data, especially spatial-temporal data, recorded in recent years demands for advanced analysis methods. Neural networks derived from self-organizing maps established themselves as a useful tool to analyse static and temporal dat...

Predicting Sprint Potential: A Machine Learning Model Based on Blood Metabolite Profiles in Young Male Athletes.

European journal of sport science
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...

Validity and Inter-Device Reliability of an Artificial Intelligence App for Real-Time Assessment of 505 Change of Direction Tests.

European journal of sport science
The present study aimed to explore the validity and inter-device reliability of a novel artificial intelligence app (Asstrapp) for real-time measurement of the traditional (tra505) and modified-505 (mod505) change of direction (COD) tests. Twenty-fiv...