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Injury Prediction in Competitive Runners With Machine Learning

S. Sofie Lövdal, Ruud J.R. Den Hartigh, and George Azzopardi

witnessed a growth in technologies and machine learning applications, which can be employed to make predictions about future performance, injuries, and thereby improve data-driven guidance in sports. 8 – 10 In the current study, we use a supervised machine learning approach, which relies principally on

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Predicting Soccer Players’ Fitness Status Through a Machine-Learning Approach

Mauro Mandorino, Jo Clubb, and Mathieu Lacome

feasibility of using machine learning (ML) techniques to assess the fitness status of soccer players within their training environment. To date, ML has been employed for numerous purposes in soccer such as injury prevention, 11 , 12 performance analysis, 13 , 14 or technical/tactical analysis. 15 This

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Sprint Assessment Using Machine Learning and a Wearable Accelerometer

Reed D. Gurchiek, Hasthika S. Rupasinghe Arachchige Don, Lasanthi C. R. Pelawa Watagoda, Ryan S. McGinnis, Herman van Werkhoven, Alan R. Needle, Jeffrey M. McBride, and Alan T. Arnholt

velocity estimation error. The MIMU methods discussed here are limited by sensor imperfections and model assumptions. In other biomechanics contexts, some employ machine learning (ML) techniques both for classification 14 – 17 and regression. 18 – 24 Mannini and Sabatini 18 estimated running speeds less

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Machine Learning in Sport Social Media Research: Practical Uses and Opportunities

James Du, Yoseph Z. Mamo, Carter Floyd, Niveditha Karthikeyan, and Jeffrey D. James

, and advanced statistical methods is readily available to re-examine and falsify the conventional assumptions and established frameworks ( Mamo et al., 2021 ). Though recent efforts have seen machine learning (ML) address such complex tasks as active leisure participation ( Du et al., 2021 ), customer

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Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review

Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan, and Lisa Mackay

but the use of cut points is still necessary when organizing these data into intensity categories. Manually defined algorithms have also been used to classify raw data into activity types with varying levels of success. 14 More recently, researchers have employed machine-learning techniques to

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

Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G.P. Frencken, Filip Staes, Jesse J. Davis, and Werner F. Helsen

Australian football (AFL) found that artificial neural networks (ANNs), a machine learning approach, more accurately predicted the RPE in response to ELIs compared with traditional statistics. 9 Other machine learning techniques could be used for this task as well, and each technique has strengths and

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Automated Classification of Postural Control for Individuals With Parkinson’s Disease Using a Machine Learning Approach: A Preliminary Study

Yumeng Li, Shuqi Zhang, and Christina Odeh

, it is still unclear which postural control variables could be used for the early detection of PD. Machine learning is a branch of artificial intelligence that enables computer systems to learn from data and analyze data without being explicitly programmed. Interest in machine learning has grown

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Relationship Between Wellness Index and Internal Training Load in Soccer: Application of a Machine Learning Model

Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia

. In recent years, data mining approaches have been gaining interest in sport science. Among these techniques, machine learning (ML) allows for the development of algorithms based on mathematical models able to discover multidimensional linear and nonlinear patterns in large data sets. 18 , 19

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A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living

Kerstin Bach, Atle Kongsvold, Hilde Bårdstu, Ellen Marie Bardal, Håkon S. Kjærnli, Sverre Herland, Aleksej Logacjov, and Paul Jarle Mork

classify different postures and physical activity types by use of rule-based algorithms ( Crowley et al., 2019 ; Skotte et al., 2014 ) or machine learning classifiers ( Arvidsson et al., 2019 ; Narayanan et al., 2020 ; Stewart et al., 2018 ). Which postures and activity types that can be detected and

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Impact of Reduced Sampling Rate on Accelerometer-Based Physical Activity Monitoring and Machine Learning Activity Classification

Scott Small, Sara Khalid, Paula Dhiman, Shing Chan, Dan Jackson, Aiden Doherty, and Andrew Price

 Hz with the AX3 accelerometer. The objectives were to: (a) identify any effect of sampling rate on device-measured activity for both overall and in specific free-living activities, (b) characterize the effect of reduced sampling rate on machine learning activity classification, and (c) develop a