Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study
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 ki...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-03-01
|
| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2023.1155542/full |
| _version_ | 1811159589607964672 |
|---|---|
| author | Florian van Dellen Florian van Dellen Florian van Dellen Nikolas Hesse Nikolas Hesse Rob Labruyère Rob Labruyère |
| author_facet | Florian van Dellen Florian van Dellen Florian van Dellen Nikolas Hesse Nikolas Hesse Rob Labruyère Rob Labruyère |
| author_sort | Florian van Dellen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T05:43:48Z |
| format | Article |
| id | doaj.art-fd6aa60aa89c4827894fecc21ed11bf4 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-04-10T05:43:48Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-fd6aa60aa89c4827894fecc21ed11bf42023-03-06T05:51:21ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442023-03-011010.3389/frobt.2023.11555421155542Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation studyFlorian van Dellen0Florian van Dellen1Florian van Dellen2Nikolas Hesse3Nikolas Hesse4Rob Labruyère5Rob Labruyère6Sensory-Motor Systems Lab, Department of Health Science and Technology, ETH Zurich, Zurich, SwitzerlandResearch Department, Swiss Children’s Rehab, University Children’s Hospital Zurich, Zurich, SwitzerlandChildren's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, SwitzerlandResearch Department, Swiss Children’s Rehab, University Children’s Hospital Zurich, Zurich, SwitzerlandChildren's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, SwitzerlandResearch Department, Swiss Children’s Rehab, University Children’s Hospital Zurich, Zurich, SwitzerlandChildren's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, SwitzerlandIntroduction: 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.https://www.frontiersin.org/articles/10.3389/frobt.2023.1155542/fulllokomatkinematicsphysiotherapy3D gait analysisazure kinect |
| spellingShingle | Florian van Dellen Florian van Dellen Florian van Dellen Nikolas Hesse Nikolas Hesse Rob Labruyère Rob Labruyère Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study Frontiers in Robotics and AI lokomat kinematics physiotherapy 3D gait analysis azure kinect |
| title | Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study |
| title_full | Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study |
| title_fullStr | Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study |
| title_full_unstemmed | Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study |
| title_short | Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study |
| title_sort | markerless motion tracking to quantify behavioral changes during robot assisted gait training a validation study |
| topic | lokomat kinematics physiotherapy 3D gait analysis azure kinect |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2023.1155542/full |
| work_keys_str_mv | AT florianvandellen markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT florianvandellen markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT florianvandellen markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT nikolashesse markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT nikolashesse markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT roblabruyere markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy AT roblabruyere markerlessmotiontrackingtoquantifybehavioralchangesduringrobotassistedgaittrainingavalidationstudy |