Machine Learning and Data Mining in Exercise, Sports and Health Research

Dear Colleagues,

ML and data mining have completely revamped the fields of exercise, sports, and health research by providing sophisticated tools to exploit massive datasets for important inferences. In exercise science, ML algorithms can handle complicated patterns in physiological responses, which then assist in developing customized training regimes. Predictive modelling contributes a lot to sports research, as coaches use such models to guide their decisions and prevent many injuries while assessing individual game performance. In addition, ML enables the investigation of health data in order to find risk factors and apply individualized interventions aimed at those particulars. The use of ML and data mining in these areas gives researchers the ability to unearth hidden connections that help improve performance optimization, injury prevention, and health care. With the increasing number of wearable technology and sensor devices, an enormous amount of data are produced, which offers 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
Guest Editor

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• data science
• sensor data
• health informatics
• sport analytics
• health data
• performance analysis
• optimization