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无标记运动跟踪用于量化机器人辅助步态训练期间的行为变化:一项验证研究。

Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study.

作者信息

van Dellen Florian, Hesse Nikolas, Labruyère Rob

机构信息

Sensory-Motor Systems Lab, Department of Health Science and Technology, ETH Zurich, Zurich, Switzerland.

Research Department, Swiss Children's Rehab, University Children's Hospital Zurich, Zurich, Switzerland.

出版信息

Front Robot AI. 2023 Mar 6;10:1155542. doi: 10.3389/frobt.2023.1155542. eCollection 2023.

DOI:10.3389/frobt.2023.1155542
PMID:36950282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10025461/
Abstract

Measuring kinematic behavior during robot-assisted gait therapy requires either laborious set up of a marker-based motion capture system or relies on the internal sensors of devices that may not cover all relevant degrees of freedom. This presents a major barrier for the adoption of kinematic measurements in the normal clinical schedule. However, to advance the field of robot-assisted therapy many insights could be gained from evaluating patient behavior during regular therapies. For this reason, we recently developed and validated a method for extracting kinematics from recordings of a low-cost RGB-D sensor, which relies on a virtual 3D body model to estimate the patient's body shape and pose in each frame. The present study aimed to evaluate the robustness of the method to the presence of a lower limb exoskeleton. 10 healthy children without gait impairment walked on a treadmill with and without wearing the exoskeleton to evaluate the estimated body shape, and 8 custom stickers were placed on the body to evaluate the accuracy of estimated poses. We found that the shape is generally robust to wearing the exoskeleton, and systematic pose tracking errors were around 5 mm. Therefore, the method can be a valuable measurement tool for the clinical evaluation, e.g., to measure compensatory movements of the trunk.

摘要

在机器人辅助步态治疗过程中测量运动行为,要么需要费力地设置基于标记的运动捕捉系统,要么依赖于设备的内部传感器,而这些传感器可能无法涵盖所有相关的自由度。这成为在常规临床流程中采用运动学测量的一个主要障碍。然而,为了推动机器人辅助治疗领域的发展,通过评估常规治疗期间的患者行为可以获得许多见解。因此,我们最近开发并验证了一种从低成本RGB-D传感器的记录中提取运动学的方法,该方法依靠虚拟3D身体模型来估计每一帧中患者的身体形状和姿势。本研究旨在评估该方法在存在下肢外骨骼的情况下的稳健性。10名无步态障碍的健康儿童在跑步机上行走,分别穿着和不穿外骨骼,以评估估计的身体形状,并在身体上放置8个定制贴纸以评估估计姿势的准确性。我们发现,身体形状通常对外骨骼的穿戴具有稳健性,系统的姿势跟踪误差约为5毫米。因此,该方法可以成为临床评估的一种有价值的测量工具,例如用于测量躯干的代偿运动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/1f34b5e56557/frobt-10-1155542-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/8e49939eb4a6/frobt-10-1155542-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/9113c3485b08/frobt-10-1155542-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/1f34b5e56557/frobt-10-1155542-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/8e49939eb4a6/frobt-10-1155542-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/9113c3485b08/frobt-10-1155542-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef2b/10025461/1f34b5e56557/frobt-10-1155542-g003.jpg

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