Lahkar Bhrigu K, Robert Thomas, Basso Fermín, Dumas Raphaël, Rosario Helios De
Kinesiology for Assessment of Sports, Health and Injury (KASHI) Lab, School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, India.
Univ Lyon, Univ Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T9406, Lyon F-69622, France.
J Biomech. 2025 Aug;189:112840. doi: 10.1016/j.jbiomech.2025.112840. Epub 2025 Jun 28.
Body segment inertial parameters (BSIPs) are critical for human movement analysis. However, child-specific BSIPs remains limited. This study aimed to develop regression models for BSIPs (mass, CoM-position, and moments of inertia) using 3D body scans from 688 children aged 2.9-12.7 years. A 3D scanning system was used to capture body surfaces as point clouds, which were automatically processed to generate segmented, personalized volumetric body meshes with embedded segment coordinate systems. These meshes were then used to compute 3D BSIPs, which were normalized (relative to body mass and corresponding segment length) and fitted by regression models separately for males and females. The regression models demonstrated high predictive accuracy for normalized mass and moderate-to-good accuracy for normalized CoM-positions and radii of gyration. Age-related changes were observed as reductions in normalized mass for the head-neck and abdomen, alongside increases for the thigh. Normalized CoM-positions shifted posteriorly for the abdomen, anteriorly for the thigh, and proximally for the forearm. Normalized radii of gyration declined across all directions, particularly for the hand and thigh. This work provides the first comprehensive BSIP regressions for a large, gender-balanced cohort of children up to 12 years old, addressing limitations in prior research with a fully automated approach. These regressions are expected to advance biomechanical modeling and enhance movement analysis in pediatric populations.
身体节段惯性参数(BSIPs)对于人体运动分析至关重要。然而,针对儿童的特定BSIPs仍然有限。本研究旨在利用688名年龄在2.9至12.7岁儿童的三维身体扫描数据,开发用于BSIPs(质量、质心位置和转动惯量)的回归模型。使用三维扫描系统将身体表面捕捉为点云,这些点云被自动处理以生成带有嵌入式节段坐标系的分段、个性化的体积身体网格。然后使用这些网格来计算三维BSIPs,将其进行归一化(相对于体重和相应节段长度),并分别针对男性和女性通过回归模型进行拟合。回归模型对归一化质量显示出高预测准确性,对归一化质心位置和回转半径显示出中等至良好的准确性。观察到与年龄相关的变化,即头颈和腹部的归一化质量减少,而大腿的归一化质量增加。归一化质心位置在腹部向后移动,在大腿向前移动,在前臂向近端移动。归一化回转半径在所有方向上均下降,尤其是手部和大腿。这项工作为多达12岁的大型、性别均衡的儿童队列提供了首个全面的BSIP回归模型,以完全自动化的方法解决了先前研究中的局限性。这些回归模型有望推动生物力学建模并加强儿科人群中的运动分析。