Online quality assessment of human movement from skeleton data

Adeline T M Paiement, Lili Tao, Sion L Hannuna, Massimo Camplani, Dima Damen, Majid Mirmehdi

Research output: Contribution to conferenceConference Paperpeer-review

60 Citations (Scopus)
517 Downloads (Pure)

Abstract

We propose a general method for online estimation of the quality of movements from Kinect skeleton data. A robust non-linear manifold learning technique is used to reduce the dimensionality of the noisy skeleton data. Then, a statistical model of normal movement is built from observations of healthy subjects, and the level of matching of new observations with this model is computed on a frame-by-frame basis following Markovian assumptions. The proposed method is validated on the assessment of gait on stairs.
Original languageEnglish
Publication statusPublished - Sept 2014
EventBritish Machine Vision Conference - University of Nottingham, Nottingham, United Kingdom
Duration: 1 Sept 20145 Sept 2014

Conference

ConferenceBritish Machine Vision Conference
Country/TerritoryUnited Kingdom
CityNottingham
Period1/09/145/09/14

Research Groups and Themes

  • Digital Health
  • SPHERE

Keywords

  • Digital Health

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  • SPHERE (EPSRC IRC)

    Craddock, I. J. (Principal Investigator), Coyle, D. T. (Principal Investigator), Flach, P. A. (Principal Investigator), Kaleshi, D. (Principal Investigator), Mirmehdi, M. (Principal Investigator), Piechocki, R. J. (Principal Investigator), Stark, B. H. (Principal Investigator), Ascione, R. (Co-Principal Investigator), Ashburn, A. M. (Collaborator), Burnett, M. E. (Collaborator), Damen, D. (Co-Principal Investigator), Gooberman-Hill, R. (Principal Investigator), Harwin, W. S. (Collaborator), Hilton, G. (Co-Principal Investigator), Holderbaum, W. (Collaborator), Holley, A. P. (Manager), Manchester, V. A. (Administrator), Meller, B. J. (Other ), Stack, E. (Collaborator) & Gilchrist, I. D. (Principal Investigator)

    1/10/1330/09/18

    Project: Research, Parent

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