AUTHOR=van Dellen Florian , Hesse Nikolas , Labruyère Rob
TITLE=Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study
JOURNAL=Frontiers in Robotics and AI
VOLUME=10
YEAR=2023
URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2023.1155542
DOI=10.3389/frobt.2023.1155542
ISSN=2296-9144
ABSTRACT=Introduction: 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.
Methods: 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.
Results & Conclusion: 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.