Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution
Abstract The development of dance movements using motion capture technology presents notable challenges, such as constraints related to body morphology, clothing interference, and the inherently nonlinear dynamics of human motion. Existing techniques generally struggle to accommodate intricate, nonl...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13060-w |
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| author | Lin Wang Yutong Liu Yucong Geng Mohammad Khishe |
| author_facet | Lin Wang Yutong Liu Yucong Geng Mohammad Khishe |
| author_sort | Lin Wang |
| collection | DOAJ |
| description | Abstract The development of dance movements using motion capture technology presents notable challenges, such as constraints related to body morphology, clothing interference, and the inherently nonlinear dynamics of human motion. Existing techniques generally struggle to accommodate intricate, nonlinear motions and encounter issues such as parameter sensitivity or prematurely becoming stuck in local solutions. This research study addresses the challenges mentioned above by developing a more precise method for reconstructing human dance movements. We develop the Two-Dimensional Matrix-Calculation (TDMC) model, combined with the Hybrid Genetic Algorithm with Fuzzy Logic Differential Evolution (HGA-FLDE), which aims to optimize the reconstruction of complex dance movements by leveraging Riemannian geometry and adaptive optimization for biomechanical nonlinear motion patterns and missing joint data. Furthermore, accuracy is achieved through other approaches, such as the Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Kinect Sensors (KS), and Evolved Deep Gated Recurrent Unit (EDGRU) models, which were all thoroughly tested against one another. Our results demonstrate that TDMC-HGA-FLDE achieves an accuracy of 0.95 at 60 nodes, outperforming LSTM (0.90), SVR (0.92), EDGRU (0.91), and Kinect Sensors (0.87). Furthermore, TDMC-HGA-FLDE achieves a minimum error of 0.39 at 20 nodes, while the other models have higher error rates. In a real-world use case of dance therapy for lower limb rehabilitation, the model reconstructed step-touch dance movements using incomplete IMU data and achieved an accuracy of 0.94 and an MSE of 0.22, outperforming all baseline models (LSTM: 0.89, 0.41; EDGRU: 0.90, 0.36; SVR: 0.91, 0.32; KS: 0.86, 0.39; TDMC: 0.88, 0.30). These results suggest that the hybrid approach significantly enhances the precision and realism of dance motion rehabilitation, making a substantial contribution to the motion capture and rehabilitation industries. |
| format | Article |
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| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-5befcef2a3e748e6882e5fb5b708373b2025-08-20T03:04:33ZengNature PortfolioScientific Reports2045-23222025-08-0115112310.1038/s41598-025-13060-wOptimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolutionLin Wang0Yutong Liu1Yucong Geng2Mohammad Khishe3Art and Sports College, Hanyang UniversityArt and Sports College, Hanyang UniversityArt and Sports College, Hanyang UniversityApplied Science Research Center, Applied Science Private UniversityAbstract The development of dance movements using motion capture technology presents notable challenges, such as constraints related to body morphology, clothing interference, and the inherently nonlinear dynamics of human motion. Existing techniques generally struggle to accommodate intricate, nonlinear motions and encounter issues such as parameter sensitivity or prematurely becoming stuck in local solutions. This research study addresses the challenges mentioned above by developing a more precise method for reconstructing human dance movements. We develop the Two-Dimensional Matrix-Calculation (TDMC) model, combined with the Hybrid Genetic Algorithm with Fuzzy Logic Differential Evolution (HGA-FLDE), which aims to optimize the reconstruction of complex dance movements by leveraging Riemannian geometry and adaptive optimization for biomechanical nonlinear motion patterns and missing joint data. Furthermore, accuracy is achieved through other approaches, such as the Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Kinect Sensors (KS), and Evolved Deep Gated Recurrent Unit (EDGRU) models, which were all thoroughly tested against one another. Our results demonstrate that TDMC-HGA-FLDE achieves an accuracy of 0.95 at 60 nodes, outperforming LSTM (0.90), SVR (0.92), EDGRU (0.91), and Kinect Sensors (0.87). Furthermore, TDMC-HGA-FLDE achieves a minimum error of 0.39 at 20 nodes, while the other models have higher error rates. In a real-world use case of dance therapy for lower limb rehabilitation, the model reconstructed step-touch dance movements using incomplete IMU data and achieved an accuracy of 0.94 and an MSE of 0.22, outperforming all baseline models (LSTM: 0.89, 0.41; EDGRU: 0.90, 0.36; SVR: 0.91, 0.32; KS: 0.86, 0.39; TDMC: 0.88, 0.30). These results suggest that the hybrid approach significantly enhances the precision and realism of dance motion rehabilitation, making a substantial contribution to the motion capture and rehabilitation industries.https://doi.org/10.1038/s41598-025-13060-wReconstructing dance movementsFuzzy logicGenetic algorithmDifferential evolution |
| spellingShingle | Lin Wang Yutong Liu Yucong Geng Mohammad Khishe Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution Scientific Reports Reconstructing dance movements Fuzzy logic Genetic algorithm Differential evolution |
| title | Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| title_full | Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| title_fullStr | Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| title_full_unstemmed | Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| title_short | Optimizing dance motion reconstruction using a two-dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| title_sort | optimizing dance motion reconstruction using a two dimensional matrix approach with hybrid genetic and fuzzy logic differential evolution |
| topic | Reconstructing dance movements Fuzzy logic Genetic algorithm Differential evolution |
| url | https://doi.org/10.1038/s41598-025-13060-w |
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