Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

Shih Wei Chen, Sheng Huang Lin, Lun De Liao, Hsin Yi Lai, Yu Cheng Pei, Te Son Kuo, Chin Teng Lin, Jyh Yeong Chang, You Yin Chen, Yu Chun Lo, Shin Yuan Chen, Robby Wu, Siny Tsang

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA).Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence.Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD.Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.

Original languageEnglish
Article number99
JournalBioMedical Engineering Online
Volume10
DOIs
Publication statusPublished - Nov 10 2011
Externally publishedYes

Fingerprint

Principal Component Analysis
Gait
Principal component analysis
Parkinson Disease
Imaging techniques
Gait analysis
Walking
Digital cameras
Pharmaceutical Preparations
Disease control
Clothing
Drug and Narcotic Control
Power spectrum
Noise abatement
Computer vision
Frequency bands
Nervous System Diseases
Feature extraction
Noise
Wear of materials

Keywords

  • Classification, quantification
  • Kernel-based principal component analysis
  • Parkinson's disease
  • Power spectrum

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Biomaterials

Cite this

Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. / Chen, Shih Wei; Lin, Sheng Huang; Liao, Lun De; Lai, Hsin Yi; Pei, Yu Cheng; Kuo, Te Son; Lin, Chin Teng; Chang, Jyh Yeong; Chen, You Yin; Lo, Yu Chun; Chen, Shin Yuan; Wu, Robby; Tsang, Siny.

In: BioMedical Engineering Online, Vol. 10, 99, 10.11.2011.

Research output: Contribution to journalArticle

Chen, SW, Lin, SH, Liao, LD, Lai, HY, Pei, YC, Kuo, TS, Lin, CT, Chang, JY, Chen, YY, Lo, YC, Chen, SY, Wu, R & Tsang, S 2011, 'Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis', BioMedical Engineering Online, vol. 10, 99. https://doi.org/10.1186/1475-925X-10-99
Chen, Shih Wei ; Lin, Sheng Huang ; Liao, Lun De ; Lai, Hsin Yi ; Pei, Yu Cheng ; Kuo, Te Son ; Lin, Chin Teng ; Chang, Jyh Yeong ; Chen, You Yin ; Lo, Yu Chun ; Chen, Shin Yuan ; Wu, Robby ; Tsang, Siny. / Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. In: BioMedical Engineering Online. 2011 ; Vol. 10.
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AU - Chang, Jyh Yeong

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