Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke

2003 ◽  
Vol 18 (1) ◽  
pp. 114-125 ◽  
Author(s):  
Sara Mulroy ◽  
JoAnne Gronley ◽  
Walt Weiss ◽  
Craig Newsam ◽  
Jacquelin Perry
Keyword(s):  
Cluster Analysis ◽  
Pattern Classification ◽  
Gait Pattern ◽  
Late Recovery

scholarly journals Gait Pattern Classification of Children with Developmental Coordination Disorder

2002 ◽  
Vol 19 (3) ◽  
pp. 378-391 ◽  
Author(s):  
Sarah J. Woodruff ◽  
Connie Bothwell-Myers ◽  
Maureen Tingley ◽  
Wayne J. Albert
Keyword(s):  
Pattern Classification ◽  
San Diego ◽  
Developmental Coordination Disorder ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Systematic Pattern ◽  
Walking Performance ◽  
Time Distance ◽  
Coordination Disorder

The purpose was to develop an index of walking performance and to examine gait pattern classifications of children with developmental coordination disorder (DCD). The San Diego database (Sutherland, Olshen, Biden, & Wyatt, 1988) provided data for our calculation of the index and for determining that the index was able to differentiate between gait variables of older (ages 3 to 7) and younger (ages 1 to 2.5) children comprising the database. We obtained cinematographical data on 17 biomechanical markers of 6 boys and 1 girl, ages 6 to 7, with DCD, during walking. Analysis of individuals with DCD gait patterns revealed that most had abnormal walking patterns. The means of the time/distance gait variables did not differ between children with DCD and San Diego children, ages 3 to 7. Children with DCD had much larger variances than other children, indicating no systematic pattern in individual gait differences.

Gait pattern classification of healthy elderly men based on biomechanical data

2000 ◽  
Vol 81 (5) ◽  
pp. 579-586 ◽  
Author(s):  
Eric Watelain ◽  
Franck Barbier ◽  
Paul Allard ◽  
André Thevenon ◽  
Jean-Claude Angué
Keyword(s):  
Pattern Classification ◽  
Gait Pattern ◽  
Elderly Men ◽  
Healthy Elderly

scholarly journals A New Classification of Diabetic Gait Pattern Based on Cluster Analysis of Biomechanical Data

2010 ◽  
Vol 4 (5) ◽  
pp. 1127-1138 ◽  
Author(s):  
Zimi Sawacha ◽  
Gabriella Guarneri ◽  
Angelo Avogaro ◽  
Claudio Cobelli
Keyword(s):  
Cluster Analysis ◽  
Gait Pattern ◽  
New Classification

Neurophysiologic classification of Wilson's disease by using cluster analysis

2006 ◽  
Vol 37 (01) ◽  
Author(s):  
W Hermann ◽  
T Villmann ◽  
HJ Kühn ◽  
P Baum ◽  
G Reichel ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Wilson’S Disease ◽  
Wilson's Disease

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

A Classification of 148 U.S. Maize Inbreds: II. Validation of Cluster Analysis Based on RFLPs

Crop Science ◽  
1994 ◽  
Vol 34 (4) ◽  
pp. 852-865 ◽  
Author(s):  
Rita Hogan Mumm ◽  
Lawrence J. Hubert ◽  
J. W. Dudley
Keyword(s):  
Cluster Analysis ◽  
Maize Inbreds

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

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