Interpreting IMU Data: Applications in Baseball Injury Analysis and Prevention

Biomechanical analysis of baseball pitching has traditionally relied on laboratory-based motion capture and high-speed video. However, wearable IMUs, containing accelerometers, gyroscopes, and sometimes magnetometers, have the potential to revolutionize on-field data collection, enabling real-time kinematic and kinetic assessments in game or practice scenarios. These technological advances are particularly crucial as they allow for continuous monitoring of the substantial mechanical stresses imposed on pitchers' elbows and shoulders, helping identify potential injury risks such as UCL tears, rotator cuff strains, and tendon overuse before they become severe. This paper provides a high-level framework for integrating IMU data with tendon-remodeling principles specific to baseball pitchers. It begins with a detailed overview of IMU sensor physics—covering sensor fusion algorithms (Kalman filters, gradient descent methods), orientation computation, and noise-filtering techniques vital for high-speed throwing motions. Next, an inverse dynamics approach is proposed to estimate joint reaction forces and soft-tissue stress, enhancing previous methods with additional equations and filtering implementations. Building on established research, the paper demonstrates how repeated loading data from IMUs can inform understanding of tendon adaptation and potential injury risk over time. Finally, a discussion of data acquisition protocols, error sources, and future directions for multi-sensor arrays and AI-based analytics, offering a roadmap for performance optimization and injury prevention. Through the integration of mathematical rigor with applied biomechanics, this paper aims to equip researchers, clinicians, and coaches with a powerful toolkit for deploying advanced wearable technology in baseball pitching.