Cluster analysis for the extraction of sagittal gait patterns in children with cerebral palsy

Gait classification in post-stroke patients using artificial neural networks

“…CA has been put to similar use in studies by other authors, i.e. for tailoring therapy based on the type of dysfunction characteristic for a given group of patients [18], for distinguishing characteristic groups of patients with pain syndromes in the vicinity of the shoulder joint [19] for identifying pathological gait patterns in children with cerebral palsy [20] and for classifying post-stroke patients [4].…”

Further evidence of validity of the Gait Deviation Index

“…The quantitative approach has employed various techniques such as cluster analysis [4], neural networks [5] and fuzzy logic [6]. However it was the development of the Gillette Gait Index (GGI), often referred to as the normalcy index [7], that was most embraced in the clinical literature.…”

Determination of gait patterns in children with spastic diplegic cerebral palsy using principal components

“…Other studies applied a systematic approach to classification involving different statistical clustering techniques to classify gait types based on gait analysis data [6][7][8][9]. Most of them used 'kmeans' cluster analysis to identify gait patterns from sagittal gait analysis data over time.…”

“…Kienast et al [7] applied the k-means cluster analysis on temporal parameters and sagittal kinematic data and determined three main gait types from 14 healthy patients and 24 spastic diplegic subjects. Toro et al [8] applied a hierarchical cluster analysis on sagittal kinematic gait data over time to 11 healthy and 56 CP children to define the optimum number of gait clusters. They detected 13 different gait patterns which were then classified as 'crouch gait', 'equinus gait' and 'other types of gait'.…”