A comparative study of pose representation and dynamics modelling for online motion quality assessment

Lili Tao, Adeline Paiement, Dima Aldamen, Majid Mirmehdi, Sion Hannuna, Massimo Camplani, Tilo Burghardt, Ian Craddock

Research output: Contribution to journalSpecial issue (Academic Journal)peer-review

51 Citations (Scopus)
562 Downloads (Pure)


Quantitative assessment of the quality of motion is increasingly in demand by clinicians in healthcare and rehabilitation monitoring of patients. We study and compare the performances of different pose representations and HMM models of dynamics of movement for online quality assessment of human motion. In a general sense, our assessment framework builds a model of normal human motion from skeleton-based samples of healthy individuals. It encapsulates the dynamics of human body pose using robust manifold representation and a first-order Markovian assumption. We then assess deviations from it via a continuous online measure. We compare different feature representations, reduced dimensionality spaces, and HMM models on motions typically tested in clinical settings, such as gait on stairs and flat surfaces, and transitions between sitting and standing. Our dataset is manually labelled by a qualified physiotherapist. The continuous-state HMM, combined with pose representation based on body-joints’ location, outperforms standard discrete-state HMM approaches and other skeleton-based features in detecting gait abnormalities, as well as assessing deviations from the motion model on a frame-by-frame basis.
Original languageEnglish
Pages (from-to)136-152
Number of pages17
JournalComputer Vision and Image Understanding
Early online date27 May 2016
Publication statusPublished - 1 Jul 2016

Structured keywords

  • Digital Health


  • Human Motion Quality
  • Human Motion Assessment
  • Continuous-State HMM Motion Analysis
  • Motion Abnormality Detection


Dive into the research topics of 'A comparative study of pose representation and dynamics modelling for online motion quality assessment'. Together they form a unique fingerprint.

Cite this