Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
10.1186/1475-925X-10-99
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://scholarbank.nus.edu.sg/handle/10635/181624 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | National University of Singapore |
| id |
sg-nus-scholar.10635-181624 |
|---|---|
| record_format |
dspace |
| spelling |
sg-nus-scholar.10635-1816242023-10-31T09:24:33Z Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis 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 LIFE SCIENCES INSTITUTE Accuracy rate Analysis approach Binary silhouettes Clinical assessments Ease of use Gait cycles Gait parameters Gait pattern Image frames Light-Colored 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 Parkinsonia algorithm article gait human methodology nonlinear system Parkinson disease pathophysiology principal component analysis reproducibility theoretical model videorecording walking Algorithms Gait Humans Models, Theoretical Nonlinear Dynamics Parkinson Disease Principal Component Analysis Reproducibility of Results Research Design Videotape Recording Walking 10.1186/1475-925X-10-99 BioMedical Engineering Online 10 99 2020-10-27T11:30:32Z 2020-10-27T11:30:32Z 2011 Article 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. https://doi.org/10.1186/1475-925X-10-99 1475925X https://scholarbank.nus.edu.sg/handle/10635/181624 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Unpaywall 20201031 |
| institution |
National University of Singapore |
| building |
NUS Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NUS Library |
| collection |
ScholarBank@NUS |
| topic |
Accuracy rate Analysis approach Binary silhouettes Clinical assessments Ease of use Gait cycles Gait parameters Gait pattern Image frames Light-Colored 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 Parkinsonia algorithm article gait human methodology nonlinear system Parkinson disease pathophysiology principal component analysis reproducibility theoretical model videorecording walking Algorithms Gait Humans Models, Theoretical Nonlinear Dynamics Parkinson Disease Principal Component Analysis Reproducibility of Results Research Design Videotape Recording Walking |
| spellingShingle |
Accuracy rate Analysis approach Binary silhouettes Clinical assessments Ease of use Gait cycles Gait parameters Gait pattern Image frames Light-Colored 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 Parkinsonia algorithm article gait human methodology nonlinear system Parkinson disease pathophysiology principal component analysis reproducibility theoretical model videorecording walking Algorithms Gait Humans Models, Theoretical Nonlinear Dynamics Parkinson Disease Principal Component Analysis Reproducibility of Results Research Design Videotape Recording Walking 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 Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| description |
10.1186/1475-925X-10-99 |
| author2 |
LIFE SCIENCES INSTITUTE |
| author_facet |
LIFE SCIENCES INSTITUTE 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 |
| format |
Article |
| author |
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 |
| author_sort |
Chen, S.-W |
| title |
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| title_short |
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| title_full |
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| title_fullStr |
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| title_full_unstemmed |
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| title_sort |
quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis |
| publishDate |
2020 |
| url |
https://scholarbank.nus.edu.sg/handle/10635/181624 |
| _version_ |
1781792570514341888 |