Harnessing Artificial Intelligence in Sports Science: Enhancing Performance, Health, and Education

The convergence of technology and innovation in sports science has ignited a revolutionary shift driven by Artificial Intelligence (AI). AI algorithms, capable of analyzing vast amounts of data, identifying patterns, and generating automated predictions, are transforming sports performance, health, and physical education across various applications.

AI-driven algorithms are revolutionizing performance analysis by providing detailed insights into athlete performance. Modern data analytics enable coaches and analysts to interpret complex patterns in athletes’ movements, tactics, and physiological responses during practice and competition. This level of detail facilitates personalized training plans, strategic adjustments, and optimized performance outcomes. In sports medicine, machine learning (ML) and deep learning (DL) accelerate diagnosis, treatment, and rehabilitation. These algorithms analyze biomechanical data, genetic predispositions, and injury history to minimize risk and enhance recovery through tailored treatment programs. AI-driven predictive models also forecast injury likelihood, enabling preventative measures to safeguard athletes’ health and longevity.

AI is significantly advancing mental health management for athletes as well. Wearable technologies equipped with ML and DL algorithms monitor vital signs, assess physical activity levels, and provide feedback on performance metrics. This data allows teachers and coaches to track student and athlete progress, identify areas for improvement, and promote healthy lifestyle practices. AI-powered gamification tools further enhance engagement and enjoyment in physical education by introducing interactive elements like leaderboards, rewards, and challenges.

This Research Topic aims to explore the transformative impact of AI on sports science, encompassing performance analysis, sports medicine, and physical education. By collecting interdisciplinary contributions, we seek to enhance knowledge and practices in these domains for improved athlete care and performance optimization. We welcome original research articles, systematic reviews, meta-analyses, case studies, and methodological papers focused on, but not limited to, the following areas:

1. AI in Performance Analysis:
o AI-driven insights into athlete performance and biomechanics.
o Development and implementation of personalized training programs.
o Tactical adjustments and performance optimization through data analytics.

2. AI in Sports Medicine:
o Applications of ML and DL in injury diagnosis and rehabilitation.
o AI-driven predictive models for injury prevention.
o Tailored treatment programs based on biomechanical and genetic data.

3. AI in Physical Education:
o Wearable technology and AI for monitoring physical activity and performance metrics.
o Use of AI to assess and enhance students’ progress and health.
o Gamification tools to increase engagement and motivation in physical education.

4. Mental Health and Wellbeing:
o AI applications for monitoring and improving athletes’ mental health.
o Integration of AI tools for holistic health management.
o Case studies on AI-driven mental health interventions in sports.

5. Ethical and Practical Considerations:
o Ethical implications of AI in sports science.
o Challenges and limitations in implementing AI technologies.
o Future perspectives and innovations in AI for sports science.

Keywords:
Performance, Health, Education, Sports, Artificial Intelligence, Machine Learning, Deep Learning

Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The convergence of technology and innovation in sports science has ignited a revolutionary shift driven by Artificial Intelligence (AI). AI algorithms, capable of analyzing vast amounts of data, identifying patterns, and generating automated predictions, are transforming sports performance, health, and physical education across various applications.

AI-driven algorithms are revolutionizing performance analysis by providing detailed insights into athlete performance. Modern data analytics enable coaches and analysts to interpret complex patterns in athletes’ movements, tactics, and physiological responses during practice and competition. This level of detail facilitates personalized training plans, strategic adjustments, and optimized performance outcomes. In sports medicine, machine learning (ML) and deep learning (DL) accelerate diagnosis, treatment, and rehabilitation. These algorithms analyze biomechanical data, genetic predispositions, and injury history to minimize risk and enhance recovery through tailored treatment programs. AI-driven predictive models also forecast injury likelihood, enabling preventative measures to safeguard athletes’ health and longevity.

AI is significantly advancing mental health management for athletes as well. Wearable technologies equipped with ML and DL algorithms monitor vital signs, assess physical activity levels, and provide feedback on performance metrics. This data allows teachers and coaches to track student and athlete progress, identify areas for improvement, and promote healthy lifestyle practices. AI-powered gamification tools further enhance engagement and enjoyment in physical education by introducing interactive elements like leaderboards, rewards, and challenges.

This Research Topic aims to explore the transformative impact of AI on sports science, encompassing performance analysis, sports medicine, and physical education. By collecting interdisciplinary contributions, we seek to enhance knowledge and practices in these domains for improved athlete care and performance optimization. We welcome original research articles, systematic reviews, meta-analyses, case studies, and methodological papers focused on, but not limited to, the following areas:

1. AI in Performance Analysis:
o AI-driven insights into athlete performance and biomechanics.
o Development and implementation of personalized training programs.
o Tactical adjustments and performance optimization through data analytics.

2. AI in Sports Medicine:
o Applications of ML and DL in injury diagnosis and rehabilitation.
o AI-driven predictive models for injury prevention.
o Tailored treatment programs based on biomechanical and genetic data.

3. AI in Physical Education:
o Wearable technology and AI for monitoring physical activity and performance metrics.
o Use of AI to assess and enhance students’ progress and health.
o Gamification tools to increase engagement and motivation in physical education.

4. Mental Health and Wellbeing:
o AI applications for monitoring and improving athletes’ mental health.
o Integration of AI tools for holistic health management.
o Case studies on AI-driven mental health interventions in sports.

5. Ethical and Practical Considerations:
o Ethical implications of AI in sports science.
o Challenges and limitations in implementing AI technologies.
o Future perspectives and innovations in AI for sports science.

Keywords:
Performance, Health, Education, Sports, Artificial Intelligence, Machine Learning, Deep Learning

Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.