The domain of sports science and healthcare has witnessed transformative advancements through the integration of cutting-edge technologies such as biomechanical modeling, wearable sensor technology, and artificial intelligence (AI). These technological advancements have equipped researchers with unparalleled tools to decode and understand intricate human movement patterns. By leveraging these sophisticated models, researchers have been able to assess injury risks with greater precision and formulate targeted intervention strategies. Additionally, emerging tools like physiological signal monitoring and virtual reality (VR) are expanding the frontier of personalized rehabilitation, offering real-time feedback and creating patient-centered therapeutic environments. Despite these advancements, significant challenges remain which necessitate a concerted effort to transition these innovations from the laboratory to practical, real-world applications, demanding interdisciplinary collaboration.
This Research Topic aims to address these challenges, directing focus on optimizing human movement, improving both athletic and clinical performance, and enhancing overall health outcomes. Key research objectives include developing adaptive biomechanical models capable of reflecting real-world variability, advancing wearable technology for accurate motion capture in varied environments, utilizing AI to integrate diverse data types for better predictive analytics, and establishing metrics for movement efficiency and rehabilitation progress. Furthermore, there is an emphasis on pioneering integrated rehabilitation frameworks that unify biomechanical insights, physiological signal data, and VR-based training to enhance motor skill recovery and neuromuscular re-education.
The scope of this Research Topic is located at the intersection of biomechanics, wearable sensor technology, and AI as applied to sports science and healthcare. We welcome articles addressing, but not limited to, the following themes:
o Biomechanical modeling: Innovations in simulating human motion across sports, clinical, and daily-living contexts.
o Wearable technology: Sensor fusion, edge computing, and ergonomic designs for real-time motion analysis and motion state prediction.
o Injury risk assessment: Multifactorial frameworks linking biomechanics, training load, and physiological markers.
o Rehabilitation technology integration: Synergizing biomechanical metrics, physiological signals, and virtual reality (VR) to design adaptive, immersive rehabilitation systems for motor recovery and neuromuscular re-education.
o Sports science applications: Performance enhancement, rehabilitation protocols, return-to-play decision-making, and sports equipment development.
o AI in bioengineering: Machine learning for gait analysis, injury prediction, and personalized feedback systems.
o Explainable AI in sport science: Leveraging interpretable models to enhance transparency, trust, and actionable insights in performance analysis and injury prevention.
Manuscript Types: Original research, reviews, case studies, methodological papers, and perspective articles. Submissions must emphasize interdisciplinary approaches and practical relevance. Preliminary validation (experimental, clinical, or computational) is required for applied studies.
Keywords: Motion analysis, Sports science and health, Biomechanical modeling, Biomechanics, Injury risk assessment, Rehabilitation engineering, Wearable technology in motion analysis, Artificial intelligence in bioengineering
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.
