Automated detection of change of direction in Basketball players using Xsens motion tracking

Abstract

In sports science, accurate tracking of athletes' movement patterns is essential for performance analysis and injury prevention. Changes of direction (COD), frequently executed during basketball games at cutting angles of around 135◦ (internal angle of 45◦), are essential for agility and high-level performance. Moreover, mastering effective COD mechanics is associated with a lower risk of injuries and enhanced long-term athletic success. However, manual segmentation of data from wearable sensors is labor-intensive and time-consuming, often creating bottlenecks for sports practitioners. The aim of this study was to evaluate the feasibility and accuracy of an automated algorithm for detecting COD movements in basketball and to compare its performance with manual detection methods. Data were collected from 62 basketball players, each completing two tests (V-cut test and a modified V-cut test), totaling 248 trials. The system utilizes kinematic data from an Xsens full-body kit to analyze key variables that characterize direction changes. The proposed method detects COD events with a median error of one frame and an interquartile range of two frames. The system demonstrated nearly 80% accuracy in COD detection, as validated against manual video analysis. These findings indicate that automated COD detection can significantly reduce segmentation time for practitioners while providing actionable, data-driven insights to enhance kinematic assessment during sport-specific activities