Ortigas-Vásquez Ariana, Einfeldt Ann-Kathrin, Haufe Yasmin, Utz Michael, Jakubowitz Eike, Sauer Adrian
Research and Development, Aesculap AG, Tuttlingen, Germany.
Department of Orthopaedic and Trauma Surgery, Musculoskeletal University Center Munich, Campus Grosshadern, Ludwig Maximilians University Munich, Munich, Germany.
Front Bioeng Biotechnol. 2025 Apr 2;13:1530365. doi: 10.3389/fbioe.2025.1530365. eCollection 2025.
Gait analysis plays a key role in improving our understanding of joint kinematics during locomotion, often by leveraging marker-based systems. Accessibility to marker-based systems is nevertheless limited, as they are usually associated with high equipment costs, large space requirements, and the need for lengthy data processing. These restrictions have therefore driven the need for tools that facilitate the interpretation and comparison of openly accessible kinematic datasets, even in cases where the data have been collected using distinct equipment and/or protocols. This study addresses variations in kinematic data arising from the use of different marker sets, focusing specifically on the tibio-femoral joint kinematics of 15 healthy subjects during treadmill walking. By simultaneously capturing joint motion using five distinct marker sets, we were able to confirm the presence of visible differences in the raw kinematic outputs prior to data optimisation, despite their representing the same underlying motion. We subsequently implemented the REference FRame Alignment MEthod (REFRAME) to account for signal differences linked to inconsistent local reference frame orientations. After REFRAME optimisation, improved convergence of the kinematic signals was observed, confirming that the differences observed in raw signals stemmed primarily from differing reference frame orientations, rather than genuine variations in joint motion. This study highlights REFRAME's potential to enhance comparability across biomechanical datasets, thus facilitating robust inter-laboratory comparisons and supporting reliable interpretations of data in clinical and research applications.
步态分析在提高我们对运动过程中关节运动学的理解方面起着关键作用,通常借助基于标记的系统。然而,基于标记的系统的可及性有限,因为它们通常伴随着高昂的设备成本、较大的空间需求以及冗长的数据处理需求。因此,这些限制促使人们需要一些工具,以便于对公开可用的运动学数据集进行解释和比较,即使在数据是使用不同设备和/或协议收集的情况下也是如此。本研究探讨了因使用不同标记集而产生的运动学数据差异,特别关注15名健康受试者在跑步机行走过程中的胫股关节运动学。通过同时使用五个不同的标记集捕捉关节运动,我们能够证实在数据优化之前,原始运动学输出中存在明显差异,尽管它们代表的是相同的基础运动。我们随后实施了参考框架对齐方法(REFRAME),以解决与不一致的局部参考框架方向相关的信号差异。在REFRAME优化之后,观察到运动学信号的收敛性得到改善,这证实了在原始信号中观察到的差异主要源于不同的参考框架方向,而不是关节运动的真正变化。本研究强调了REFRAME在增强生物力学数据集之间可比性方面的潜力,从而便于进行可靠的实验室间比较,并支持临床和研究应用中数据的可靠解释。