In the burgeoning field of sports science, effective monitoring and optimization of athletic performance have become pivotal for developing successful training interventions. Traditionally, methodologies like kinematic analysis with marker-based motion capture, force plate evaluations, and lab-centered physiological assessments have been used. Such methods, while precise, demand specialized settings and substantial time investment, confining their application to controlled environments and limiting sample size scalability. The applicability of these traditional practices in real-world sports scenarios remains an ongoing challenge, necessitating more accessible solutions to overcome limitations related to facility dependency and resource allocation.

Recent developments in computing technologies have begun to address these constraints by introducing novel, more flexible evaluation methods. Tools like markerless motion capture systems, wearable sensors, portable force plates, and digital data collection platforms (such as mobile apps and online questionnaires) represent a paradigm shift towards real-world, large-scale, and long-term performance monitoring. These advancements not only enhance data collection efficiency but also broaden the potential for in-depth data analysis, offering a new horizon for big-data integration in sports science. However, while these technologies provide cost-effective alternatives to traditional methods, the full breadth of their capabilities—such as real-time feedback, seamless data analysis, and the potential for adaptive sports coaching—remains underexplored. Furthermore, challenges persist regarding the accuracy, reliability, and comprehensiveness of measurements, which need to be addressed to expand their research and practical applications.

This Research Topic aims to delve into the potential of emerging computing technologies for sports performance monitoring and optimization. We seek to uncover the transformative impacts of these innovations, from data-driven decision-making in training to tactical optimization and injury prevention.

To gather further insights in this multidisciplinary field, we welcome articles addressing, but not limited to, the following themes:

o Technological advancements, including new hardware, sensor systems, or algorithms aimed at enhancing real-world data collection processes.

o Advanced analytics incorporating big data, machine learning, or modeling to refine performance analysis and improve training outcomes.

o Applied interventions where computing technologies augment coaching practices and athlete development, including real-time data processing.

o Cross-disciplinary collaborations uniting sports science, engineering, and data science for comprehensive research outputs.

o Both empirical and methodological studies demonstrating practical real-world impact for sports scientists and practitioners.

Are you interested in contributing a manuscript to this Research Topic?

Next-generation Computing in Sports: Redefining Performance Monitoring and Optimization

Topic Coordinator