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

Abstract

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.

More information

Identifiers

ORCID for Cheryl Metcalf: orcid.org/0000-0002-7404-6066

Catalogue record

Export record

Altmetrics