Machine Learning in Sports Medicine
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Sport and Health".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 367
Special Issue Editors
Interests: artificial intelligence; machine learning; action recognition; data predication; mobile robot; reinforcement learning
2. Physical Activity and Performance Institute, Konkuk University, Seoul 05029, Republic of Korea
Interests: altitude training; hypoxic conditioning; exercise physiology and intervention; athletic performance; obesity; hypertension; metabolic syndrome; sarcopenia; mental disorder; mild cognitive impairment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear colleagues,
The International Journal of Environmental Research and Public Health is organizing a Special Issue to present recent research achievements in the field of sports medicine using machine learning techniques.
Machine learning is a branch of artificial intelligence that is designed to classify and predict data without explicit instructions by processing large datasets using computational algorithms. In the field of sports medicine, ML algorithms have demonstrated great potential in predicting outcomes of sports injuries, analysing a correlation between modifiable factors and outcomes and evaluating the future injury risks. The literature has shown that the performance of ML algorithms could exceed that of conventional statistical techniques and presented the potential to improve data-driven insights and automate medical tasks. There are a lot of aspects that should be considered to apply ML algorithms in sports medicine, such as the accuracy of computational algorithms, quality of the training datasets, preparation of large datasets, and metrics for performance evaluation. For this Special Issue, we seek papers that present recent research achievements using ML algorithms in sports medicine and discuss various aspects of ML algorithms.
This Special Issue is open to any subject area related to machine learning in sports medicine. The topics related to computational algorithms, performance comparison of various ML algorithms, collection of large datasets, and implementation of ML algorithms in sports medicine applications are welcome for submission. The listed keywords suggest just a few of the many possible topics for papers submitted to this Special Issue.
Dr. Hoeryong Jung
Dr. Hun-Young Park
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- deep learning
- artificial intelligence
- support vector machine
- multiple linear regression
- random forest
- neural network
- sports medicine
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