Research on Data Collection and Posture Optimization in Dance Training Using Smart Wearable Devices

Dance training combines artistic creativity with technical precision, requiring a unique blend of body mechanics, rhythmic synchronization, and expressive movement. As an intricate art form, it challenges dancers to not only master physical technique but also convey deep emotional resonance through their performance. Traditional training methods often lack the capability to provide precise, personalized feedback, especially for complex choreography and high-dimensional motion data, leaving room for subjective interpretation and inconsistent improvement. To address these challenges, this study proposes a novel framework utilizing smart wearable devices and a computational model, the Dynamic Dance Representation Network (DDRN). DDRN integrates pose estimation, rhythm alignment, and expressiveness evaluation to analyze dance movements with unparalleled accuracy. The system processes multi-modal inputs, including motion data and audio signals, to generate actionable feedback on precision, rhythm synchronization, and artistic expression. By identifying subtle discrepancies in movement and rhythm, it offers real-time insights that enable targeted adjustments. Complemented by the Artistic Alignment Strategy (AAS), the framework optimizes training outcomes by balancing technical accuracy and creative interpretation. Experimental results demonstrate that DDRN and AAS significantly improve movement quality and personalization in dance training, offering an innovative tool for modern education, advanced performance enhancement, and the democratization of high-level artistic coaching. This approach bridges the gap between technology and artistry, redefining the boundaries of dance training and performance analysis for the next generation of dancers.