Coll Isabel, Mavor Matthew P, Karakolis Thomas, Graham Ryan B, Clouthier Allison L
Ottawa-Carleton Institute of Biomedical Engineering (OCIBME), Faculty of Engineering, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON, K1N 6N5, Canada.
School of Human Kinetics, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON, K1N 6N5, Canada.
Ann Biomed Eng. 2025 Feb;53(2):358-370. doi: 10.1007/s10439-024-03622-w. Epub 2024 Oct 7.
Field performance of modern soldiers is affected by an increase in body-borne load due to technological advancements related to their armour and equipment. In this project, the Theia3D markerless motion capture system was compared to the marker-based gold standard for capturing movement patterns of participants wearing various body-borne loads. The aim was to estimate lower body joint kinematics, gastrocnemius lateralis and medialis muscle activation patterns, and lower body joint reaction forces from the two motion capture systems. Data were collected on 16 participants performing three repetitions of walking and running under four body-borne load conditions by both motion capture systems simultaneously. A complete musculoskeletal analysis was completed in OpenSim. Strong correlations ( ) and acceptable differences were observed between the kinematics of the marker-based and markerless systems. Timing of muscle activations of the gastrocnemius lateralis and medialis, as estimated through OpenSim from both systems, agreed with the ones measured using electromyography. Joint reaction force results showed a very strong correlation ( ) between the systems; however, the markerless model estimated greater joint reaction forces when compared the marker-based model due to differences in muscle recruitment strategy. Overall, this research highlights the potential of markerless motion capture to track participants wearing body-borne loads.
由于与士兵的护甲和装备相关的技术进步，现代士兵身上携带的负荷增加，这影响了他们的战场表现。在这个项目中，将Theia3D无标记运动捕捉系统与基于标记的金标准进行了比较，以捕捉佩戴各种身上携带负荷的参与者的运动模式。目的是估计来自这两种运动捕捉系统的下肢关节运动学、腓肠肌外侧头和内侧头的肌肉激活模式以及下肢关节反作用力。两个运动捕捉系统同时收集了16名参与者在四种身上携带负荷条件下进行三次行走和跑步重复的数据。在OpenSim中完成了完整的肌肉骨骼分析。基于标记和无标记系统的运动学之间观察到了强相关性（ ）和可接受的差异。通过OpenSim从两个系统估计的腓肠肌外侧头和内侧头的肌肉激活时间与使用肌电图测量的时间一致。关节反作用力结果显示两个系统之间有非常强的相关性（ ）；然而，由于肌肉募集策略的差异，与基于标记的模型相比，无标记模型估计的关节反作用力更大。总体而言，这项研究突出了无标记运动捕捉在跟踪身上携带负荷的参与者方面的潜力。
