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Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation

Record type: Article

Dynamic movements of the hand, fingers and thumb are difficult to measure due to the versatility and complexity of movement inherent in function. An innovative approach to measuring hand kinematics is proposed and validated. The proposed system utilises the Microsoft KinectTM and goes beyond gesture recognition, to develop a validated measurement technique of finger kinematics. The proposed system adopted landmark definition (validated through ground truth estimation against assessors) and grip classification algorithms, including kinematic definitions (validated against a laboratory-based motion capture system). The results of the validation show 78% accuracy when identifying specific markerless landmarks. In addition, comparative data with a previously validated kinematic measurement technique show accuracy of MCP±10° (average absolute error (AAE) = 2.4°), PIP±12° (AAE = 4.8°) and DIP±11° (AAE = 4.8°). These results are notably better than clinically based alternative manual measurement techniques. The ability to measure hand movements, and therefore functional dexterity, without interfering with underlying composite movements, is the paramount objective to any bespoke measurement system. The proposed system is the first validated markerless measurement system using the Microsoft KinectTM that is capable of measuring finger joint kinematics. It is suitable for home-based motion capture for the hand and therefore achieves this objective.

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Citation

Metcalf, Cheryl, Robinson, Rebecca, Malpass, Adam J., Bogle, Tristan P., Dell, Thomas A., Harris, Chris and Demain, Sara H. (2013) Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation IEEE Transactions on Biomedical Engineering, 60, (8), pp. 2184-2192. (doi:10.1109/TBME.2013.2250286). (PMID:23475333).

More information

e-pub ahead of print date: 7 March 2013
Published date: August 2013
Keywords: hand kinematics, markerless, Microsoft Kinect, telerehabilitation
Organisations: Faculty of Health Sciences

Identifiers

Local EPrints ID: 354739
URI: http://eprints.soton.ac.uk/id/eprint/354739
ISSN: 0018-9294
PURE UUID: 9a3dea49-208a-4530-b590-9e4083d0e729
ORCID for Cheryl Metcalf: ORCID iD orcid.org/0000-0002-7404-6066

Catalogue record

Date deposited: 18 Jul 2013 15:02
Last modified: 27 Jul 2017 16:30

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Contributors

Author: Cheryl Metcalf ORCID iD
Author: Rebecca Robinson
Author: Adam J. Malpass
Author: Tristan P. Bogle
Author: Thomas A. Dell
Author: Chris Harris
Author: Sara H. Demain

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