Bio-Kinetic Analysis for Youth Athlete Injury Prevention
An AI-powered biomechanics framework designed to detect movement inefficiencies, predict injury risk, and deliver real-time corrective feedback before injuries occur.
Dr. Elena Popova
Leading researcher in robotic-assisted kinesiotherapy, injury prevention systems and youth athlete development with over 15 years of research experience.
Core Research Architecture
The framework combines computer vision, wearable sensor fusion and predictive AI models to evaluate athlete performance and identify injury patterns before they become clinically visible.
Motion Tracking
Tracks skeletal alignment, acceleration, asymmetry and joint behaviour through high-resolution movement capture.
AI Risk Engine
Generates probability scores for future injuries based on biomechanical deviations and fatigue indicators.
Performance Dashboard
Provides coaches and researchers with real-time analytics and longitudinal performance trends.
Intervention System
Recommends corrective actions that reduce injury risk and improve movement efficiency.
Data Collection
Multi-sensor capture from youth athletes across training environments.
Model Training
Neural network development using annotated biomechanical datasets.
Validation Phase
Cross-laboratory testing with European sports institutes.
Deployment
Integration into athlete monitoring and coaching platforms.
Interested in Collaborating?
Connect with the research team, discuss partnership opportunities, or explore future studies at ICSE 2026.
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