Wang Lin, Liu Yutong, Geng Yucong, Khishe Mohammad
Art and Sports College, Hanyang University, Seoul, 04763, South Korea.
Applied Science Research Center, Applied Science Private University, Amman, Jordan.
Sci Rep. 2025 Aug 13;15(1):29736. doi: 10.1038/s41598-025-13060-w.
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.
利用动作捕捉技术开发舞蹈动作存在显著挑战，例如与身体形态、服装干扰以及人体运动固有的非线性动力学相关的限制。现有技术通常难以适应复杂的非线性运动，并且会遇到诸如参数敏感性或过早陷入局部解等问题。本研究通过开发一种更精确的方法来重建人类舞蹈动作，解决了上述挑战。我们开发了二维矩阵计算（TDMC）模型，并结合了具有模糊逻辑差分进化的混合遗传算法（HGA-FLDE），旨在通过利用黎曼几何和对生物力学非线性运动模式及缺失关节数据的自适应优化，来优化复杂舞蹈动作的重建。此外，通过其他方法实现了准确性，例如长短期记忆（LSTM）、支持向量回归（SVR）、Kinect传感器（KS）和进化深度门控循环单元（EDGRU）模型，所有这些模型都相互进行了全面测试。我们的结果表明，TDMC-HGA-FLDE在60个节点处的准确率达到0.95，优于LSTM（0.90）、SVR（0.92）、EDGRU（0.91）和Kinect传感器（0.87）。此外，TDMC-HGA-FLDE在20个节点处的最小误差为0.39，而其他模型的误差率更高。在下肢康复舞蹈治疗的实际应用案例中，该模型使用不完整的惯性测量单元（IMU）数据重建了踏步舞蹈动作，准确率达到0.94，均方误差为0.22，优于所有基线模型（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）。这些结果表明，这种混合方法显著提高了舞蹈动作康复的精度和真实感，为动作捕捉和康复行业做出了重大贡献。
