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

51 Citations (Scopus)
398 Downloads (Pure)


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


ConferenceBritish Machine Vision Conference
Country/TerritoryUnited Kingdom

Structured keywords

  • Digital Health


  • Digital Health


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