Clinical assessment of freezing of gait in Parkinson's disease from computer-generated animation

Tiffany R. Morris, Catherine Cho, Valentina Dilda, James M. Shine, Sharon L. Naismith, Simon J G Lewis, Steven T. Moore*

*Corresponding author for this work

Research output: Contribution to journalArticle

19 Citations (Scopus)


The current 'gold standard' for clinical evaluation of freezing of gait (FOG) in Parkinson's disease (PD) is determination of the number of FOG episodes from video by independent raters. We have previously described a robust technique for objective FOG assessment from lower-limb acceleration. However, there is no existing method for validation of autonomous FOG measures in the absence of video documentation. In this study we compared the results of clinical evaluation of FOG from computer-generated animations (derived from body-mounted inertial sensors) during a timed up and go test with the 'gold standard' of clinical video assessment, utilizing a cohort of 10 experienced raters from four PD centers. Agreement between the 10 clinical observers for scoring of FOG from computer animations was more robust for the relative duration of freeze events (percent time frozen; intraclass correlation coefficient of 0.65) than number of FOG episodes, and was comparable with clinical evaluation of the patient from video (intraclass correlation coefficient 0.73). This result suggests that percent time frozen should be considered (along with number of FOG events) to better convey FOG severity. The ability of clinical observers to quantify FOG from computer-generated animation derived from lower-limb motion data provides a potential approach to validation of accelerometry-based FOG identification outside of the clinic.

Original languageEnglish
Pages (from-to)326-329
Number of pages4
JournalGait and Posture
Issue number2
Publication statusPublished - Jun 2013
Externally publishedYes


  • Accelerometry
  • Avatar
  • FOG
  • Timed up-and-go task

Fingerprint Dive into the research topics of 'Clinical assessment of freezing of gait in Parkinson's disease from computer-generated animation'. Together they form a unique fingerprint.

Cite this