A vision-based analysis system for gait recognition in patients with Parkinson's disease

Chien Wen Cho, Wen Hung Chao, Sheng Huang Lin, You Yin Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

112 Scopus citations


Recognition of specific Parkinsonian gait patterns is helpful in the diagnosis of Parkinson's disease (PD). However, there are few computer-aided methods to identify the specific gait patterns of PD. We propose a vision-based diagnostic system to aid in recognition of the gait patterns of Parkinson's disease. The proposed system utilizes an algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA). This scheme not only addresses the high data dimensionality problem during image processing but also distinguishes different gait categories simultaneously. The feasibility of the proposed system for the recognition of PD gait was tested by using gait videos of PD and normal subjects. The efficiency of feature extraction using PCA and LDA coefficients are also compared. Experimental results showed that LDA had a recognition rate for Parkinsonian gait of 95.49%, which is higher than the conventional PCA feature extraction method. The proposed system is a promising aid in identifying the gait of Parkinson's disease patients and can discriminate the gait patterns of PD patients and normal people with a very high classification rate. Crown

Original languageEnglish
Pages (from-to)7033-7039
Number of pages7
JournalExpert Systems with Applications
Issue number3 PART 2
StatePublished - Apr 2009


  • Gait analysis
  • Linear discriminant analysis (LDA)
  • Parkinson's disease
  • Principal component analysis (PCA)
  • Vision-based


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