AIMC Topic: Athletic Performance
Clear Filters Showing 81 to 90 of 97 articles
Evaluation of tactical training in team handball by means of artificial neural networks.
Journal of sports sciences
May 21, 2016
While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to asses...
A minimum attention control law for ball catching.
Bioinspiration & biomimetics
Oct 6, 2015
Digital implementations of control laws typically involve discretization with respect to both time and space, and a control law that can achieve a task at coarser levels of discretization can be said to require less control attention, and also reduce...
Influence of recovery intensity on oxygen demand and repeated sprint performance.
The Journal of sports medicine and physical fitness
Sep 11, 2015
BACKGROUND: This study aimed to determine effects of recovery intensity (passive, 20%, 30% and 40% V̇O2peak) on oxygen uptake kinetics, performance and blood lactate accumulation during repeated sprints.
A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.
PloS one
Sep 9, 2015
Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled wit...
Team activity recognition in Association Football using a Bag-of-Words-based method.
Human movement science
Mar 25, 2015
In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) techniqu...
Effects of robotically modulating kinematic variability on motor skill learning and motivation.
Journal of neurophysiology
Feb 11, 2015
It is unclear how the variability of kinematic errors experienced during motor training affects skill retention and motivation. We used force fields produced by a haptic robot to modulate the kinematic errors of 30 healthy adults during a period of p...
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
Jun 1, 2025
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...
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...
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
Feb 1, 2025
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...
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