Fast NURBS-based parametric modeling of human calves with high-accuracy for personalized design of graduated compression stockings

Abstract

Background and objectives: Accurate human body models are increasingly demanded by high-quality human-centered ergonomic applications, especially the design and manufacturing of compressive functional apparels. However, existing parametric models in related works are not capable to accurately describe detailed local shape features of human. Methods: In this work, a high-accuracy parametric modeling approach for human limb was proposed. 3D Scans of human calves were studied. Key data points of the scanned human calves were identified according to human anatomy, forming a quasi-triangular mesh of feature points. Then, non-uniform rational B-splines (NURBS) method was implemented. Control points were calculated from the key data points, with which the human calf shapes can be reconstructed by the smooth NURBS surface, giving rise to a new parametric model of human calves. Error between the scanned and reconstructed calf shapes were analyzed to verify the effectiveness of this model. Results: Error analysis showed that, this proposed method delivers a high-efficiency and high-accuracy parametric shape modeling approach with averaged error observed as only 0.37% for all the 260 subjects, much less compared to previous relative works (around 5%). For tentative application, customized medical compression stockings were designed based on this model and proved as valid to exert desired gradient compression on the according calf mannequin. Conclusions: By introducing the non-uniform rational B-splines method, a parametric model capable of characterizing human limbs with high-accuracy was proposed. Using very small amount of data, this model is expected to highly facilitate remote customized design and provide 3D shape references for design of compressive garments. Moreover, the proposed methods can inspire developments of other mixed modeling methods for high-accuracy applications.