AIMC Topic: Physical Conditioning, Human
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Subjective recovery in professional soccer players: A machine learning and mediation approach.
Journal of sports sciences
Feb 5, 2025
Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 101 male professional soccer players of 4 Italian Serie C teams and to further investigate whether th...
Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.
Journal of sports science & medicine
Sep 1, 2024
Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players fro...
Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.
Research quarterly for exercise and sport
Jul 3, 2024
To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice ses...
Association between match-related physical activity profiles and playing positions in different tasks: A data driven approach.
Journal of sports sciences
Apr 4, 2024
Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a...
Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.
International journal of sports physiology and performance
Feb 24, 2024
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...
Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis.
Research in sports medicine (Print)
Dec 26, 2023
This study verified the relationship between internal load (IL) and external load (EL) and their association on injury risk (IR) prediction considering machine learning (ML) approaches. Studies were included if: (1) participants were male professiona...
A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction.
Computational intelligence and neuroscience
Mar 7, 2022
This paper examines the problem of athletes' training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and pl...
Analysing the predictive capacity and dose-response of wellness in load monitoring.
Journal of sports sciences
Jan 6, 2021
This study aimed to identify the predictive capacity of wellness questionnaires on measures of training load using machine learning methods. The distributions of, and dose-response between, wellness and other load measures were also examined, offerin...
Affect
Physical Conditioning, Human
Perception
Fatigue
Sports
Soccer
Myalgia
Cricket Sport
Physical Exertion
Football
Motivation
Sleep
Wearable Electronic Devices
Machine Learning
Health Status
Geographic Information Systems
Regression Analysis
Surveys and Questionnaires
Decision Trees
Stress, Psychological
Humans
Land-walking vs. water-walking interventions in older adults: Effects on aerobic fitness.
Journal of sport and health science
Dec 23, 2019
BACKGROUND: Low cardiorespiratory fitness is an independent predictor of all-cause and cardiovascular mortality, and interventions that increase fitness reduce risk. Water-walking decreases musculoskeletal impact and risk of falls in older individual...
Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models.
Journal of sports sciences
Feb 6, 2019
We aimed to compare multilayer perceptron (MLP) neural networks, radial basis function neural networks (RBF) and linear models (LM) accuracy to predict the centre of mass (CM) horizontal speed at low-moderate, heavy and severe swimming intensities us...
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