Reconstruction of Traditional Dance Movements with Hidden Markov Model Multimodal

Abstract

The preservation of traditional dance in the digital era requires a computational representation that not only captures bodily motion, but also maintains the stylistic continuity and cultural embodiment embedded within its choreographic structure. This study presents a comparative evaluation of three emission models in Hidden Markov Models (HMM)—Single-Gaussian, Gaussian Mixture Model (GMM-HMM), and Multinomial HMM—for reconstructing the Bedoyo Majapahit classical dance using markerless motion capture data. The recorded 3D skeleton sequence consists of 3,341 frames and 33 joint coordinates per frame, which were normalized and filtered using a low-pass smoothing technique. Principal Component Analysis (PCA) was then applied to reduce dimensionality while preserving the primary spatial-temporal variance of the motion features. The reconstruction performance was assessed using three quantitative metrics: Mean Squared Error (MSE) to measure geometric fidelity, Dynamic Time Warping (DTW) to evaluate temporal consistency, and Fréchet distance to assess global trajectory similarity. The results show that the GMM-HMM significantly outperforms the other two models across all evaluation metrics, demonstrating superior stability, curvature preservation, and alignment with the original motion pattern. The findings highlight that traditional choreography exhibits inherently multimodal motion characteristics, which are better captured by Gaussian mixture emissions rather than unimodal or discretized representations. Beyond numeric superiority, the GMM-HMM also preserves stylistic curvature that reflects key embodied principles of Javanese dance—wiraga (embodied form), wirama (temporal flow), and wirasa (expressive intention). This study provides a methodological foundation for culturally faithful digital preservation and serves as a basis for future AI-assisted interactive learning systems for traditional dance heritage.