A machine learning approach to assessing gait patterns for Complex Regional Pain Syndrome

Mingjing Yang, Huiru Zheng, Haiying Wang, Sally McClean, Jane Hall, Nigel Harris

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


Complex Regional Pain Syndrome (CRPS) is a condition that causes a long-term burning pain in a limb or part of a limb and it can cause various degrees of the physical functional performance deterioration. Objective assessment of physical functional performance of patients is one critical component to evaluate the therapy outcome for CRPS. This paper aims to investigate the feasibility of assessing the physical performance of patients with Complex Regional Pain Syndrome based on the analysis of gait data recorded by an accelerometer in short walking distances. Ten subjects with CRPS and ten control subjects were recruited. Thirty three features were extracted from each recording. A machine learning method, Multilayer perceptron neural-networks (MLP), was applied to classify the normal and abnormal gait patterns from data obtained on a 2.4 m performance evaluation test. The best classification accuracy (99.38%) was achieved using 3 features selected by a step-wise-forward method. To further validate its performance, an independent test set including 14 cases extracted from data obtained on a 20 m performance evaluation test was adopted. A prediction accuracy of 85.7% was obtained.
Original languageEnglish
Pages (from-to)740-746
JournalMedical Engineering and Physics
Issue number6
Publication statusPublished (in print/issue) - Jul 2012


  • Accelerometer
  • Gait analysis
  • Complex Regional Pain Syndrome
  • Feature extraction
  • Classification


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