LLM-Based Modeling of 3D Joint Kinematics of the Polonaise Folk Dance for Motion-to-Text Generation for User-Feedback Capabilities
Artykuł w czasopiśmie
MNiSW
40
Lista 2024
| Status: | |
| Autorzy: | Wójcik Emilia Stefania, Alencynowicz Maciej, Skublewska-Paszkowska Maria, Powroźnik Paweł, Barszcz Marcin, Dziedzic Krzysztof |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 37 |
| Numer artykułu: | e70130 |
| Impact Factor: | 1,7 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The analysis and learning of complex human movements require precise motion capture, data processing, and clear feedback. This paper presents a comprehensive pipeline for real-time movement analysis and personalized feedback generation based on motion capture data and large language models. Three-dimensional motion data are captured using an 8-camera Vicon motion capture system and processed using both a custom joint angle computation method designed to support real-time streaming and Vicon Plug-in Gait angles. The extracted kinematic features are normalized and structured into movement patterns that represent key elements of dance choreography. Based on these patterns, synthetic performance samples are generated and used to fine-tune large language models capable of interpreting movement performance and producing natural-language feedback. Models fine-tuned in this study include Mistral 7B, H2O-Danube 3 4B, and Qwen 2.5 3B. The proposed approach enables the generation of context-aware, descriptive guidance that goes beyond numerical scores and supports motor learning. Experimental results demonstrate that H2O-Danube 3 4B can provide accurate and efficient feedback while remaining suitable for interactive and real-time applications. The presented framework offers a scalable foundation for intelligent movement training systems and can be extended to other movement disciplines and immersive virtual reality environments. |
