Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke.
The mixture of gait deviations seen in patients following a stroke is remarkably variable. An objective system for classification of gait patterns for this population could be used to guide treatment planning. Quantitated gait analysis was conducted for 47 individuals at admission to in-patient rehabilitation and again at 6 months post-stroke for 42 subjects. Non-hierarchical cluster analysis was used to classify the gait patterns of patients based on the temporal-spatial and kinematic parameters of walking. Four clusters of patients were identified at both assessment intervals. At the admission test walking velocity, peak knee extension in mid stance and peak dorsiflexion in swing were the three factors that best characterized the groups. At 6 months the explanatory variables were velocity, knee extension in terminal stance, and knee flexion in pre swing. Differences in muscle strength and muscle activation patterns during walking were identified between groups.
Mulroy
Sara
S
Pathokinesiology Laboratory, Rancho Los Amigos National Rehabilitation Center, 7601 East Imperial Highway, Bldg. 800, Room 33, Downey, CA 90242, USA. smulroy@dhs.co.la.ca.us
Gronley
JoAnne
J
Weiss
Walt
W
Newsam
Craig
C
Perry
Jacquelin
J
D000328:Adult; D000368:Aged; D000843:Ankle Joint / Q000502:physiology; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D004576:Electromyography; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007719:Knee Joint / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000502:physiology; D020521:Stroke / Q000503:physiopathology*