Design and development of graduated compression garment with adhesive stripes using 3D virtual fitting technology
Source abstract
Abstract
Purpose The goal of this research is to utilize 3D virtual fitting technology to design and develop a customized full leg sleeve with graduated compression and strategically placed adhesive stripes, aiming to improve training effectiveness by restricting bending movement in the leg. Design/methodology/approach Knitted fabric and polymer adhesive film were chosen for compression garment development. The design of adhesive stripes was based on Kinesio taping method. Pattern design and 3D visualization was performed using Modaris 3D (CAD Lectra). The compression values were determined by the Laplace equation. The compression full leg sleeve with and without the adhesive stripes were fitted on the virtual mannequin in the squat position in order to determine the influence of adhesive stripes for tensile load in longitudinal direction. Findings The compression values in a virtual garment varied from 23.17 ± 1.94 mmHg in the ankle girth to 13.41 ± 1.50 mmHg in the thigh girth. The difference did not exceed 3 mmHg compared to the planned pressure values. The obtained result showed that purposefully placed adhesive stripes significantly increased the tensile force in the longitudinal direction of the garment in a squat position, so it may improve training effectiveness. Research limitations/implications 3D virtual fitting represents the fit on a solid body form, which may affect the accuracy of compression garment simulation, as the human body is not rigid. Originality/value The application of 3D virtual fitting technology in creating customized and functional garments demonstrates its unique contribution to garment prototyping and product development.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Ergonomics and Musculoskeletal Disorders · 3D Shape Modeling and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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