Riemannian Geometric Approaches for Measuring Movement Quality

Anirudh Som, Rushil Anirudh, Qiao Wang, Pavan Turaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

A growing set of applications in home-based interactive physical therapy require the ability to monitor, inform and assess the quality of everyday movements. Interactive therapy requires both real-time feedback of movement quality, as well as summative feedback of quality over a period of time. Obtaining labeled data from trained experts is the main limitation, since it is both expensive and time consuming. Motivated by recent studies in motor-control, we propose an unsupervised approach that measures movement quality of simple actions by considering the deviation of a trajectory from an ideal movement path in the configuration space. We use two different configuration spaces to demonstrate this idea - the product space S1×S1 to model the interaction of two joint angles, and SE(3)×SE(3) to model the movement of two joints, for two different applications in movement quality estimation. We also describe potential applications of these ideas to assess quality in real-time.

Original languageEnglish (US)
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PublisherIEEE Computer Society
Pages1005
Number of pages1
ISBN (Electronic)9781467388504
DOIs
StatePublished - Dec 16 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, United States
Duration: Jun 26 2016Jul 1 2016

Other

Other29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
CountryUnited States
CityLas Vegas
Period6/26/167/1/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Som, A., Anirudh, R., Wang, Q., & Turaga, P. (2016). Riemannian Geometric Approaches for Measuring Movement Quality. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 (pp. 1005). [7789619] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2016.129