Research on the Influence of Dynamic Fit of Dance Costumes based on Computer Vision and Pressure Sensing on Stage Performance

Wangfeifei Dong

Article
2025 / Volume 8 / Pages 1163-1178
Received 6 August 2025; Accepted 22 August 2025; Published 30 December 2025
https://doi.org/10.31881/TLR.2025.1163

Abstract
Traditional static assessment fails to capture the critical garment–body interactions during dance. This study investigates the influence of a costume’s dynamic fit on stage performance using a novel, synergistic methodology that integrates computer vision with pressure sensing to provide a holistic, quantitative assessment of garment–body dynamics. Two leotards, a “Standard Fit” (SF) and an “Optimized Fit” (OF) designed using 3D scanning and dynamic pattern engineering, were tested on ten elite ballet dancers. During a series of standardized movements, we synchronously measured interface pressure, garment–body gap, and biomechanical performance kinematics. The OF garment demonstrated a significantly superior dynamic fit, achieving a 28% reduction in peak shoulder pressure and reducing fabric gapping in the lumbar region compared with the SF garment (p < 0.01). These physical improvements correlated directly with enhanced performance, including a 5.7% greater hip abduction angle in the grand jeté and a 4.2% higher peak angular velocity in the pirouette (p < 0.05). Dancers’ subjective ratings were also substantially higher in the OF leotard, with a 67% improvement in perceived freedom of movement and an 85% improvement in comfort. This research establishes a quantitative, data-driven link between specific dynamic fit characteristics and tangible performance gains, providing a new evidence-based paradigm for the design of high-performance functional apparel.

Keywords
wearable sensors, e-textiles, dynamic fit, computer vision, pressure performance analysis