Please use this identifier to cite or link to this item:
Title: Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
Authors: Chen, S.-W
Lin, S.-H
Liao, L.-D 
Lai, H.-Y
Pei, Y.-C
Kuo, T.-S
Lin, C.-T
Chang, J.-Y
Chen, Y.-Y
Lo, Y.-C
Chen, S.-Y
Wu, R
Tsang, S
Keywords: Accuracy rate
Analysis approach
Binary silhouettes
Clinical assessments
Ease of use
Gait cycles
Gait parameters
Gait pattern
Image frames
Low-cost solution
Lower frequencies
Monocular video
Motor function
Parkinson's disease
Principal Components
Stride length
Vision based
Walking velocity
Computer aided analysis
Computer vision
Digital cameras
Frequency bands
Gait analysis
Medical computing
Neurodegenerative diseases
Power spectrum
Video cameras
Principal component analysis
nonlinear system
Parkinson disease
principal component analysis
theoretical model
Models, Theoretical
Nonlinear Dynamics
Parkinson Disease
Principal Component Analysis
Reproducibility of Results
Research Design
Videotape Recording
Issue Date: 2011
Citation: Chen, S.-W, Lin, S.-H, Liao, L.-D, Lai, H.-Y, Pei, Y.-C, Kuo, T.-S, Lin, C.-T, Chang, J.-Y, Chen, Y.-Y, Lo, Y.-C, Chen, S.-Y, Wu, R, Tsang, S (2011). Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. BioMedical Engineering Online 10 : 99. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2011 Chen et al; licensee BioMed Central Ltd.
Source Title: BioMedical Engineering Online
ISSN: 1475925X
DOI: 10.1186/1475-925X-10-99
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_1475-925X-10-99.pdf2.01 MBAdobe PDF




checked on Apr 7, 2021

Page view(s)

checked on Apr 8, 2021

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons