A digital twin-based biomechanical and psychosocial coupling framework for university sports dance training and evaluation

The digital twin-based framework presented in this study integrates both the biomechanical analysis and psychological assessment thus improving the sports dance training in the university sector. The synchronizing of the 3D modeling and the real-time data collection from the Latin and modern dance curricula is used by the system to capture the students’ physical performance and psychological development. Of the total, 118 participants were evaluated through various datasets, with the biomechanical indicators (for example, stability of joints, balance, and efficiency of movement) analyzed alongside the psychological scales that are validated. The findings show that there are very strong associations between the movement that is very fluid and the happiness, the core strength and the emotional regulation, and the dance coordination and the self-confidence. The model which has been put forth is a considerable advance over the classic clustering methods-namely MLAPW and MLKMeans-monetarily by accuracy, global optimization, and interpretability. The pairing of Hierarchical Affinity Propagation (HAP) with the digital twin framework resulted in a maximum of 17.6% increase in the correctness of clustering and a 15.3% decrease in average distance error across multi-level coupling when compared with the most successful baseline, respectively. It is an interdisciplinary methodology that allows the use of personalized, data-driven sports dance instruction and at the same time demonstrates the digital twin technology’s capability of developing the whole person, both physically and mentally, in the educational context.