Analisis Pergerakan Lengan Tari Bedoyo Majapahit Berbasis Motion Capture
Abstract
The Bedoyo Majapahit dance is an Indonesian cultural heritage that combines elegance and meaning in every movement, particularly in its signature arm movements. This study focuses on exploring and analyzing arm movement patterns in this traditional dance using motion capture technology, aiming to document, understand, and scientifically reveal the beauty and dynamics of these movements. Positional data of the shoulders, elbows, and wrists from both sides were analyzed to calculate speed and elbow angles. Descriptive statistics, correlation analysis, and K-Means clustering were applied to identify dominant patterns. The results indicate a positive correlation between shoulder and elbow speeds on both sides, with correlation values of 0.71 and 0.84, highlighting symmetrical movements. Three movement clusters were identified: low, medium, and high, with the low cluster being the most dominant. The average speeds in the low cluster were 0.097 m/s for the shoulders, 0.092 m/s for the elbows, and 0.091 m/s for the wrists, reflecting the gentle characteristics of the dance. Meanwhile, the medium and high clusters exhibited higher speed values, particularly for the wrists in the high cluster at 1.888 m/s, indicating more dynamic movements. This study provides a quantitative understanding of the smooth and symmetrical movements in Bedoyo Majapahit dance, supporting cultural preservation through data-driven analytical approaches.
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References
Kuswarsantyo, APRESIASI BUDAYA. Lingkaran, 2019.
W. W. T. Lam, Y. M. Tang, and K. N. K. Fong, “A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation,” J. Neuroeng. Rehabil., vol. 20, no. 1, pp. 1–26, Dec. 2023, doi: 10.1186/S12984-023-01186-9/TABLES/2.
H. ZHENG, R. SHIOYA, C. KATO, and K. HARADA, “マーカーレスモーションキャプチャによる舞台衣装を着用時の動作推定,” Proc. Mech. Eng. Congr. Japan, vol. 2023, no. 0, pp. S235-01, 2023, doi: 10.1299/JSMEMECJ.2023.S235-01.
M. Wang and R. Yu, “Digital production and realization for traditional dance movements based on Motion Capture Technology,” Front. Soc. Sci. Technol., vol. 4, no. 11, pp. 13–18, Dec. 2022, doi: 10.25236/FSST.2022.041102.
K. Sun, “Research on Dance Motion Capture Technology for Visualization Requirements,” Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/2062791.
X. Jiang, “Application of motion capture technology based on dance big data in dance retrieval,” Appl. Math. Nonlinear Sci., vol. 8, no. 2, pp. 2927–2938, Jul. 2023, doi: 10.2478/AMNS.2023.2.00009.
H. Bhuyan, J. Killi, J. K. Dash, P. P. Das, and S. Paul, “Motion Recognition in Bharatanatyam Dance,” IEEE Access, vol. 10, pp. 67128–67139, 2022, doi: 10.1109/ACCESS.2022.3184735.
M. Yoshimura, H. Murasato, T. Kai, A. Kuromiya, K. Yokoyama, and K. Hachimura, “Analysis of Japanese dance movements using motion capture system,” Syst. Comput. Japan, vol. 37, no. 1, pp. 71–82, Jan. 2006, doi: 10.1002/SCJ.20250.
N. Nakano et al., “Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras,” Front. Sport. Act. living, vol. 2, May 2020, doi: 10.3389/FSPOR.2020.00050.
D. D. Vo and R. Butler, “A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets,” Jan. 2022, Accessed: Nov. 04, 2024. [Online]. Available: https://arxiv.org/abs/2201.02503v1
R. R. Bini, F. A. Moura, P. R. P. Santiago, S. Colyer, and N. Vanicek, “Special issue themes: Markerless motion analysis in sport and exercise,” J. Sports Sci., vol. 42, no. 1, pp. 1–2, Jan. 2024, doi: 10.1080/02640414.2024.2317652.
M. Pardell et al., “Movement Outcomes Acquired via Markerless Motion Capture Systems Compared with Marker-Based Systems for Adult Patient Populations: A Scoping Review,” Biomech. 2024, Vol. 4, Pages 618-632, vol. 4, no. 4, pp. 618–632, Oct. 2024, doi: 10.3390/BIOMECHANICS4040044.
