Résumé
This study examines how musical expertise, tempo, and beat division influence synchronization accuracy and regularity in two movement tasks: finger tapping (discrete movements) and arm swing (continuous movements). Using a markerless motion capture system, we analyzed synchronization metrics across different rhythmic conditions. Motion data were extracted via AI-based pose estimation, and synchronization was computed by aligning movement peaks with beat times detected from audio stimuli. Results show that musicians exhibit higher synchronization accuracy and consistency than non-musicians, particularly in finger tapping tasks. Furthermore, simpler beat structures (binary rhythms) and moderate tempos facilitate better synchronization, whereas increased rhythmic complexity and tempo variability reduce performance. Interestingly, finger tapping leads to more precise synchronization than arm swing, suggesting that movement type significantly impacts rhythmic alignment. These findings support applications in therapy, training, and interactive systems, and demonstrate the value of AI-based motion tracking for scalable rhythm analysis.