Machine Learning and Data Mining in Exercise, Sports and Health Research, Second Edition
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 549
Special Issue Editors
Interests: sports injuries; athletic injuries; return to play; trauma; sport medicine; sport rehabilitation; physical therapy; rehabilitation; readaptation; injury prevention; injury epidemiology; disability; recovery; endurance; trail running; cycling; assessment
Special Issues, Collections and Topics in MDPI journals
Interests: exercise physiology; muscle oxygenation; hypoxia; high performance; strength and conditioning in football/soccer
Special Issue Information
Dear Colleagues,
ML and data mining have significantly transformed the fields of exercise, sports, and health research by providing sophisticated tools which can leverage large datasets to gain critical insights. In exercise science, ML algorithms can handle complex patterns in physiological responses, thereby assisting in developing customized training regimens. Predictive modelling significantly contributes to sports research, as coaches use these models to guide their decisions, prevent injuries, and assess individual game performance. In addition, ML enables the analysis of health data to identify risk factors and apply individualized interventions tailored to those factors. The use of ML and data mining in these areas enables researchers to uncover hidden connections that improve performance, injury prevention, and healthcare. With the increasing number of wearable technology and sensor devices, an enormous amount of data are produced, offering many new possibilities for improving the models and developing in-depth knowledge about human physiology. This interdisciplinary approach holds great potential to influence the shape of exercise, sports, and health research in the future, which will lead to precision, efficiency, and evidence-based decision-making.
Dr. Daniel Rojas-Valverde
Dr. Aldo Alfonso Vasquez-Bonilla
Prof. Dr. Rodrigo Yánez-Sepúlveda
Guest Editors
Manuscript Submission Information
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Keywords
- data science
- sensor data
- health informatics
- sport analytics
- health data
- performance analysis
- optimization
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