Artificial Intelligence in Sports Injury and Injury Prevention

A special issue of Sports (ISSN 2075-4663).

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 18094

Special Issue Editor


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Guest Editor
Discipline of Exercise and Sport Science, University of Newcastle, Ourimbah, Australia
Interests: training; adaptation; sport; prediction; environmental; nutrition; endurance; performance

Special Issue Information

Dear Colleagues,

In the recent past, there has been rapid growth in new hardware and related technologies for the collection of data during training and competition at all levels of sport. This has enabled coaches and athletes to analyze performance in far greater depth and from different perspectives. This has also led to far greater opportunities to use large data to make decisions about competitive strategies or to predict performance. Artificial intelligence (AI) has been used in sport to make valid predictions of game outcomes. In competitions, decisions can be rapidly made to influence team performance. The prediction of maladaptation to training and injury prevention is also an important consideration in athletic preparation. With far greater amounts of data being collected by athletes at all levels, AI in combination with instruments of data collection can be used to optimize athletic preparation in a more efficient and valid way. This Special Issue will highlight new and emerging approaches to the analysis of large data for the purpose of reducing injury risk and maladaptation to training and to optimize athletic training and performance. Original research, review articles and case studies from the field will be considered

Dr. David J Bentley
Guest Editor

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Keywords

  • data
  • machine learning
  • portable
  • instrumentation
  • GPS
  • team sport
  • injury
  • overtraining
  • recovery
  • intervention

Published Papers (3 papers)

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Research

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11 pages, 1194 KiB  
Article
Sex Difference in Running Stability Analyzed Based on a Whole-Body Movement: A Pilot Study
by Arunee Promsri
Sports 2022, 10(9), 138; https://doi.org/10.3390/sports10090138 - 16 Sep 2022
Cited by 6 | Viewed by 1768
Abstract
A sex-specific manner in running tasks is considered a potential internal injury risk factor in runners. The current study aimed to investigate the sex differences in running stability in recreational runners during self-preferred speed treadmill running by focusing on a whole-body movement. To [...] Read more.
A sex-specific manner in running tasks is considered a potential internal injury risk factor in runners. The current study aimed to investigate the sex differences in running stability in recreational runners during self-preferred speed treadmill running by focusing on a whole-body movement. To this end, principal component analysis (PCA) was applied to kinematic marker data of 22 runners (25.7 ± 3.3 yrs.; 12 females) for decomposing the whole-body movements of all participants into a set of principal movements (PMs), representing different movement synergies forming together to achieve the task goal. Then, the sex effects were tested on three types of PCA-based variables computed for individual PMs: the largest Lyapunov exponent (LyE) as a measure of running variability; the relative standard deviation (rSTD) as a measure of movement structures; and the root mean square (RMS) as a measure of the magnitude of neuromuscular control. The results show that the sex effects are observed in the specific PMs. Specifically, female runners have lower stability (greater LyE) in the mid-stance-phase movements (PM45) and greater contribution and control (greater rSTD and RMS) in the swing-phase movement (PM1) than male runners. Knowledge of an inherent sex difference in running stability may benefit sports-related injury prevention and rehabilitation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sports Injury and Injury Prevention)
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15 pages, 2158 KiB  
Article
Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players
by Mauro Mandorino, António J. Figueiredo, Gianluca Cima and Antonio Tessitore
Sports 2022, 10(1), 3; https://doi.org/10.3390/sports10010003 - 24 Dec 2021
Cited by 11 | Viewed by 4821
Abstract
This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was [...] Read more.
This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was applied to record all MSI during the season. Anthropometric data, predicted age at peak height velocity, and skeletal age were collected. The session-RPE method was daily employed to quantify internal training/match load, and monotony, strain, and cumulative load over the weeks were calculated. A countermovement jump (CMJ) test was submitted before and after each training/match to quantify players’ neuromuscular fatigue. All these data were used to predict the risk of MSI through different data mining models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). Among them, SVM showed the best predictive ability (area under the curve = 0.84 ± 0.05). Then, Decision tree (DT) algorithm was employed to understand the interactions identified by the SVM model. The rules extracted by DT revealed how the risk of injury could change according to players’ maturity status, neuromuscular fatigue, anthropometric factors, higher workloads, and low recovery status. This approach allowed to identify MSI and the underlying risk factors. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sports Injury and Injury Prevention)
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Review

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16 pages, 611 KiB  
Review
A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer
by Alessio Rossi, Luca Pappalardo and Paolo Cintia
Sports 2022, 10(1), 5; https://doi.org/10.3390/sports10010005 - 24 Dec 2021
Cited by 29 | Viewed by 9706
Abstract
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport [...] Read more.
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sports Injury and Injury Prevention)
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