ABSTRACT
Basketball is a physically demanding sport characterized by rapid accelerations, abrupt decelerations, frequent jumping, and sudden changes in direction, all of which impose significant strain on the musculoskeletal system and elevate the risk of injury. To address this challenge, this paper proposes a theoretical framework for smart monitoring of player safety through camera-based pose estimation. The framework focuses on the real-time detection and recognition of movement patterns, with particular emphasis on distinguishing biomechanically safe actions from potentially hazardous ones.
By leveraging skeletal key point extraction, spatiotemporal feature modeling, and machine learning-based classification, the system aims to identify high-risk movements and generate actionable feedback for injury prevention and performance optimization. The framework also outlines conceptual strategies for addressing data imbalance, improving classification robustness, and evaluating model performance using appropriate metrics. This foundational approach serves as a basis for future implementation in athlete monitoring systems, with potential applications in training, rehabilitation, and competitive gameplay analysis.
Keywords: Basketball, Player Safety, Pose Estimation, Camera-Based Monitoring, Machine Learning, Movement Recognition, Theoretical Framework, Injury Prevention
INTRODUCTION
Basketball is a high-intensity sport that demands a wide range of dynamic movements, including vertical jumps, rapid pivots, abrupt stops, and swift directional changes. These actions exert substantial biomechanical stress on the lower extremities particularly the knees and ankles making them susceptible to acute injuries such as ligament tears, muscle strains, and joint sprains. The ability to detect unsafe movement patterns in real-time is critical not only for mitigating injury risk but also for informing targeted training interventions and enhancing overall athletic performance.
This study introduces a camera-based theoretical framework for monitoring basketball-specific movements through pose estimation techniques. Utilizing frameworks such as Open Pose, skeletal key points corresponding to major joints are extracted from video sequences to construct a spatiotemporal representation of player motion. These key points serve as the foundation for conceptual movement modeling, enabling the system to distinguish between biomechanically safe and potentially hazardous actions.
By integrating principles from biomechanics, human activity recognition, and machine learning, the proposed framework offers a structured approach to player safety monitoring. It aims to support future development of intelligent systems capable of real-time feedback, injury prevention, and performance optimization in basketball and other high-impact sports.
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