Killen Bryce A, Van Rossom Sam, Burg Fien, Vander Sloten Jos, Jonkers Ilse
Human Movement Biomechanics Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.
Materialise Motion, Materialise, Leuven, Belgium.
Front Bioeng Biotechnol. 2024 Feb 23;12:1351403. doi: 10.3389/fbioe.2024.1351403. eCollection 2024.
Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency. We developed an workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20). Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively. : The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure.
矫正鞋垫适用于一系列足部畸形,通常基于主观评估进行设计,这限制了个性化定制,并可能导致治疗效果欠佳。在鞋垫设计和定制中融入相关技术可能会改善个性化矫正效果,从而提高鞋垫效率。我们开发了一种鞋垫设计和定制工作流程,结合测量的运动捕捉、逆肌肉骨骼建模以及正向模拟方法,以预测特定鞋垫设计的运动学响应。所开发的工作流程在27名参与者身上进行了测试,其中包括健康参与者(7名)和平足畸形患者(20名)。对于距跟关节倾斜、距跟关节前后轴、第一跖跗关节和第五跖跗关节,测量运动学与模拟运动学之间的平均误差分别为4.7±3.1、4.5±3.1、2.3±2.3和2.3±2.7°。所开发的工作流程相对于以前的建模工作流程具有明显优势,例如使用速度快、使用更易获取的数据、仅使用开源软件且高度自动化。它为未来通过调整当前实施的鞋垫模型并纳入诸如足底压力等额外数据来提高预测准确性的工作提供了坚实基础。