Probabilistic gait classification in children with cerebral palsy: A Bayesian approach

Leen Van Gestel, Tinne De Laet, Enrico Di Lello, Herman Bruyninckx, Guy Molenaers, Anja Van Campenhout, Erwin Aertbeliën, Mike Schwartz, Hans Wambacq, Paul De Cock, Kaat Desloovere

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Three-dimensional gait analysis (3DGA) generates a wealth of highly variable data. Gait classifications help to reduce, simplify and interpret this vast amount of 3DGA data and thereby assist and facilitate clinical decision making in the treatment of CP. CP gait is often a mix of several clinically accepted distinct gait patterns. Therefore, there is a need for a classification which characterizes each CP gait by different degrees of membership for several gait patterns, which are considered by clinical experts to be highly relevant. In this respect, this paper introduces Bayesian networks (BN) as a new approach for classification of 3DGA data of the ankle and knee in children with CP. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. Furthermore, they provide an explicit way of introducing clinical expertise as prior knowledge to guide the BN in its analysis of the data and the underlying clinically relevant relationships. BNs also enable to classify gait on a continuum of patterns, as their outcome consists of a set of probabilistic membership values for different clinically accepted patterns. A group of 139 patients with CP was recruited and divided into a training- (n= 80% of all patients) and a validation-dataset (n= 20% of all patients). An average classification accuracy of 88.4% was reached. The BN of this study achieved promising accuracy rates and was found to be successful for classifying ankle and knee joint motion on a continuum of different clinically relevant gait patterns.

Original languageEnglish (US)
Pages (from-to)2542-2552
Number of pages11
JournalResearch in Developmental Disabilities
Issue number6
StatePublished - Nov 2011

Bibliographical note

Funding Information:
Leen Van Gestel received a PhD fellowship of the Research Foundation-Flanders (FWO), Belgium. Tinne De Laet is a Postdoctoral Fellow of the Research Foundation-Flanders (FWO), Belgium. The authors gratefully acknowledge the European FP7 project Rosetta (FP7-230902, Robot control for skilled execution of tasks in natural interaction with humans; based on autonomy, cumulative knowledge and learning) and the IPSA project (060799) which was supported by the IWT Flanders. There was no role of these funding bodies in the study design, collection and analysis of data, interpretation of data and writing of the paper.


  • 3D gait analysis
  • Bayesian
  • Cerebral palsy
  • Children
  • Classification
  • Machine learning
  • Probabilistic
  • Psychomotor development
  • Walking


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