Abstract
This thesis investigates the synchronization between musical rhythm and human motion, focusing on sensorimotor synchronization (SMS). The research explores the relationship between rhythmic auditory stimuli and human movements, such as finger tapping, arm swings, and walking. It examines the impact of rhythmic complexity, movement types, and individual factors like musical expertise and physical morphology on synchronization accuracy. Additionally, it presents a cost-effective framework for synchronization analysis using AI-based rhythm extraction, and video motion capture tools. The proposed methodology integrates advanced signal processing techniques to analyze rhythm and movement, supported by real-time data acquisition and synchronization scoring systems. The study’s findings contribute to various fields, including music education, rehabilitation, and interactive technology development, by providing insights into how rhythm influences human movement. Furthermore, this work explores the effects of external perturbations, such as metronome shifts, on synchronization performance in different movement tasks, offering valuable perspectives for future research in both academic and applied contexts.