AI Medical Compendium Journal:
International journal of sports physiology and performance

Showing 1 to 10 of 10 articles

Predicting Field-Sport Distances Without Global Positioning Systems in Indoor Play: A Comparative Study of Machine-Learning Techniques.

International journal of sports physiology and performance
PURPOSE: Accurately predicting the distance covered by athletes during indoor sport activities without the use of GPS (global positioning systems) presents a significant challenge. This study evaluates the effectiveness of various machine-learning te...

An Educational Review on Machine Learning: A SWOT Analysis for Implementing Machine Learning Techniques in Football.

International journal of sports physiology and performance
PURPOSE: The abundance of data in football presents both opportunities and challenges for decision making. Consequently, this review has 2 primary objectives: first, to provide practitioners with a concise overview of the characteristics of machine-l...

Football Movement Profile-Based Creatine-Kinase Prediction Performs Similarly to Global Positioning System-Derived Machine Learning Models in National-Team Soccer Players.

International journal of sports physiology and performance
PURPOSE: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile ...

Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.

International journal of sports physiology and performance
PURPOSE: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3) to analy...

Injury Prediction in Competitive Runners With Machine Learning.

International journal of sports physiology and performance
PURPOSE: Staying injury free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data-science applications could provide important insights. Our purpose was to use machine learning for the pred...

Use of Machine Learning to Model Volume Load Effects on Changes in Jump Performance.

International journal of sports physiology and performance
PURPOSE: To use an artificial neural network (ANN) to model the effect of 15 weeks of resistance training on changes in countermovement jump (CMJ) performance in male track-and-field athletes.

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

International journal of sports physiology and performance
PURPOSE: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (R...

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

International journal of sports physiology and performance
CONTEXT: Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not p...

Predicting Future Perceived Wellness in Professional Soccer: The Role of Preceding Load and Wellness.

International journal of sports physiology and performance
PURPOSE: The influence of preceding load and future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external load (EL) and internal load (IL) for different time frames in combination with prese...