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

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

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

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

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Publication Type:: Journal Article
Citation:: BioMedical Engineering Online, 2011, 10
Issue Date:: 2011-11-10

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Field	Value	Language
dc.contributor.author	Chen, SW	en_US
dc.contributor.author	Lin, SH	en_US
dc.contributor.author	Liao, LD	en_US
dc.contributor.author	Lai, HY	en_US
dc.contributor.author	Pei, YC	en_US
dc.contributor.author	Kuo, TS	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Chang, JY	en_US
dc.contributor.author	Chen, YY	en_US
dc.contributor.author	Lo, YC	en_US
dc.contributor.author	Chen, SY	en_US
dc.contributor.author	Wu, R	en_US
dc.contributor.author	Tsang, S	en_US
dc.date.available	2011-11-10	en_US
dc.date.issued	2011-11-10	en_US
dc.identifier.citation	BioMedical Engineering Online, 2011, 10	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116310
dc.description.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.	en_US
dc.relation.ispartof	BioMedical Engineering Online	en_US
dc.relation.isbasedon	10.1186/1475-925X-10-99	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Parkinson Disease	en_US
dc.subject.mesh	Gait	en_US
dc.subject.mesh	Walking	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Nonlinear Dynamics	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Research Design	en_US
dc.subject.mesh	Videotape Recording	en_US
dc.title	Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

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.

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