View in EDS

Assessing single camera markerless motion capture with OpenSim inverse kinematics during upper limb activities of daily living.

Saved in:
Bibliographic Details
Title: Assessing single camera markerless motion capture with OpenSim inverse kinematics during upper limb activities of daily living.
Authors: Scott, Bradley1 (AUTHOR) b.scott.20@abdn.ac.uk, McInnes, Mhairi2 (AUTHOR), Chadwick, Edward K.2 (AUTHOR), Blana, Dimitra1 (AUTHOR)
Source: International Biomechanics. Dec2025, Vol. 12 Issue 1, p35-47. 13p.
Subjects: Motion capture (Human mechanics), Kinect (Motion sensor), Forelimb, Error analysis in mathematics, Kinematics, Measurement of angles (Geometry)
Abstract: This study evaluates the accuracy of single camera markerless motion capture (SCMoCap) using Microsoft's Azure Kinect, enhanced with inverse kinematics (IK) via OpenSim, for upper limb movement analysis. Twelve healthy adults performed ten upper-limb tasks, recorded simultaneously by OptiTrack (marker-based) and Azure Kinect (markerless) from frontal and sagittal views. Joint angles were calculated using two methods: (1) direct kinematics based on body coordinate frames and (2) inverse kinematics using OpenSim's IK tool with anatomical keypoints. Accuracy was evaluated using root mean square error (RMSE) and Bland-Altman analysis. Results indicated that the IK method slightly improved joint angle agreement with OptiTrack for simpler movements, with an average RMSE of 8° for shoulder elevation in the sagittal plane compared to 9° with the coordinate frame method. However, both methods had higher RMSEs for rotational measurements, with IK and coordinate frame methods at 21° for shoulder rotation in the sagittal plane. Forearm pronation-supination measurements were unreliable due to tracking limitations. These findings suggest that Kinect with IK improves accuracy for simpler movements but struggles with rotational joint mechanics. Future research should focus on enhancing markerless tracking algorithms to fully realise the benefits of IK. [ABSTRACT FROM AUTHOR]
Copyright of International Biomechanics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:This study evaluates the accuracy of single camera markerless motion capture (SCMoCap) using Microsoft's Azure Kinect, enhanced with inverse kinematics (IK) via OpenSim, for upper limb movement analysis. Twelve healthy adults performed ten upper-limb tasks, recorded simultaneously by OptiTrack (marker-based) and Azure Kinect (markerless) from frontal and sagittal views. Joint angles were calculated using two methods: (1) direct kinematics based on body coordinate frames and (2) inverse kinematics using OpenSim's IK tool with anatomical keypoints. Accuracy was evaluated using root mean square error (RMSE) and Bland-Altman analysis. Results indicated that the IK method slightly improved joint angle agreement with OptiTrack for simpler movements, with an average RMSE of 8° for shoulder elevation in the sagittal plane compared to 9° with the coordinate frame method. However, both methods had higher RMSEs for rotational measurements, with IK and coordinate frame methods at 21° for shoulder rotation in the sagittal plane. Forearm pronation-supination measurements were unreliable due to tracking limitations. These findings suggest that Kinect with IK improves accuracy for simpler movements but struggles with rotational joint mechanics. Future research should focus on enhancing markerless tracking algorithms to fully realise the benefits of IK. [ABSTRACT FROM AUTHOR]
ISSN:23335432
DOI:10.1080/23335432.2025.2556187