Published July 11, 2020
| public
Journal Article
Automated gap-filling for marker-based biomechanical motion capture data
Abstract
Marker-based motion capture presents the problem of gaps, which are traditionally processed using motion capture software, requiring intensive manual input. We propose and study an automated method of gap-filling that uses inverse kinematics (IK) to close the loop of an iterative process to minimize error, while nearly eliminating user input. Comparing our method to manual gap-filling, we observe a 21% reduction in the worst-case gap-filling error (p < 0.05), and an 80% reduction in completion time (p < 0.01). Our contribution encompasses the release of an open-source repository of the method and interaction with OpenSim.
Additional Information
© 2020 Taylor & Francis. Received 27 Mar 2020, Accepted 28 Jun 2020, Published online: 11 Jul 2020. The authors would like to thank Dean Molinaro for sharing the full body motion capture data that was analyzed in this paper, and Bharat Kanwar and Will Flanagan for their discussions during the conception of the gap-filling method. This work was supported, in part, by a Fulbright fellowship awarded to Jonathan Camargo-Leyva. No potential conflict of interest was reported by the author(s).Additional details
- Eprint ID
- 104437
- Resolver ID
- CaltechAUTHORS:20200720-080500097
- Fulbright Foundation
- Created
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2020-07-20Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field