McConnochie Grace, Fox Aaron, Bellenger Clint, Thewlis Dominic
Centre for Orthopaedic Trauma and Research, Adelaide Medical School, University of Adelaide, Australia.
Institute for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Australia.
J Biomech. 2025 Jan;179:112439. doi: 10.1016/j.jbiomech.2024.112439. Epub 2024 Dec 12.
Biomechanical analysis is increasingly being undertaken in field-based settings, often using inertial sensors or video-based pose estimation. These advancements necessitate more practical and accessible scaling methods as alternatives to traditional laboratory-based techniques like optical marker-based scaling. LiDAR scanning is a technique that could provide a reliable and efficient means of scaling biomechanical models. This study tested a scaling method for OpenSim models and comparing outcomes with those of traditional marker-based scaling in healthy adult participants. An anatomical skeleton was inferred from a LiDAR scan taken with an iPad. Key skeletal landmarks were then used to generate scaling factors using statistical shape models. The scaling factors of the pelvis, femur and tibia body segments derived from the LiDAR-based method demonstrated excellent reliability, with repeated scans of seven subjects producing an ICC value of 0.961. When comparing the scaling factors of eight additional subjects with the current gold standard technique of marker-based optical motion capture, a Bland-Altman analysis revealed differences of -0.5% ± 5.3 (95CI = [-10.8, 10]). Joint kinematics calculated using LiDAR scaled models had an average RMSD of 3.7° ± 0.1°when compared with those calculated with a marker-scaled model. These results indicate that a LiDAR-based scaling method can address the challenge of accurate and reliable scaling methods that are practical for use in the field. Future work with larger cohorts and diverse populations, and scaling of other body segments will provide further validation and enhance the generalizability and robustness of this approach.
生物力学分析越来越多地在基于实地的环境中进行,通常使用惯性传感器或基于视频的姿态估计。这些进展需要更实用、更易获取的缩放方法,以替代传统的基于实验室的技术,如基于光学标记的缩放。激光雷达扫描是一种可以提供可靠且高效的生物力学模型缩放方法的技术。本研究测试了一种用于OpenSim模型的缩放方法,并将结果与健康成年参与者中传统的基于标记的缩放结果进行比较。通过用iPad进行的激光雷达扫描推断出解剖骨骼。然后使用统计形状模型,利用关键骨骼标志点生成缩放因子。基于激光雷达的方法得出的骨盆、股骨和胫骨身体节段的缩放因子显示出极佳的可靠性,对七名受试者进行重复扫描得出的组内相关系数(ICC)值为0.961。当将另外八名受试者的缩放因子与当前基于标记的光学运动捕捉的金标准技术进行比较时,布兰德-奥特曼分析显示差异为-0.5%±5.3(95%置信区间=[-10.8, 10])。与使用基于标记缩放的模型计算出的关节运动学相比,使用基于激光雷达缩放的模型计算出的关节运动学平均均方根偏差(RMSD)为3.7°±0.1°。这些结果表明,基于激光雷达的缩放方法可以应对准确可靠且适用于实地的缩放方法所面临的挑战。未来针对更大队列和多样化人群的研究以及对其他身体节段的缩放将提供进一步验证,并增强这种方法的普遍性和稳健性。