title: Mixture-Model Clustering of Pathological Gait Patterns.
abstract: This study applies mixture-model clustering to spatiotemporal gait parameters in order to characterize the pathological gait pattern and to generate a composite measure indicative of overall gait performance. Gait data from 68 adults with stroke (age: 61.5 ± 13.6 years) and 20 healthy adults (age: 28.8 ± 7.1 years) were used in this study. Participants performed three passes across a GAITRite mat at different time points following stroke (poststroke adults only). Mixture-model clustering grouped participants' gait patterns based on their spatiotemporal gait features including symmetry, speed, and variability. Mixture-models with different covariance matrix parameterizations and numbers of clusters were examined. The selected clustering model successfully categorized participants' spatiotemporal gait data into three clinically meaningful groups. Based on the clustering results, gait speed, and variability measures varied across the three groups. Individuals in Group 1 are all symmetric and had the fastest and lowest gait velocity and variability, respectively. As expected, healthy participants were assigned to Group 1. All gait parameters were at an intermediate level in Group 2 and worse condition in Group 3. Moreover, resulting cluster centers were in line with previously published clinical studies on gait. In addition to clustering, each individual was given an indexed membership (ranged 0-1) to each of three groups. These indexed memberships were proposed as a single measure to encompass information about multiple gait parameters (symmetry, speed, and variability) and as a measure that is sensitive and responsive to improvement or deterioration and rehabilitation over time.
authors.forename: Elham
authors.initials: E
authors.lastname: Dolatabadi
authors.forename: Avril
authors.initials: A
authors.lastname: Mansfield
authors.forename: Kara K
authors.initials: KK
authors.lastname: Patterson
authors.forename: Babak
authors.initials: B
authors.lastname: Taati
authors.forename: Alex
authors.initials: A
authors.lastname: Mihailidis
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020233:Gait Disorders, Neurologic / Q000145:classification* / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D055815:Young Adult