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2 2016

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Gait patterns classification based on cluster and bicluster analysis

Pauk J., Minta-Bielecka K.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 2

391--396

Gait patterns of hemiplegia patients have many potential applications such as assistance in diagnosis or clinical decision-making. Many techniques were developed to classify gait patterns in past years; however, these methods have some limitations. The main goal of the study was to present the performance evaluation results of the new biclustering algorithm called KMB. The second objective was to compare clustering and biclustering methods. The study was performed based on the gait patterns of 41 hemiplegia patients over 12 months post-stroke, at the age of 48.6 ± 19.6 years. Spatial–temporal gait parameters and joint moments were measured using motion capture system and force plates. Clustering and biclustering algorithms were applied for data consisting of joint moments of lower limbs. The obtained results of this study based on joint moments, clustering, and biclustering can be applied to evaluate patient condition and treatment effectiveness. We suggest that the biclustering algorithm compared to clustering algorithms better characterizes the specific traits and abnormalities of the joint moments, especially in case of hemiplegia patients.

A new classification of hemiplegia gait patterns based on bicluster analysis of joint moments

Pauk J, Minta-Bielecka K.

Acta of Bioengineering and Biomechanics

2016

Vol. 18, no. 4

33--40

Purpose: Hemiplegia is a paralysis on one side of the body resulting from disease or injury to the motor centers of the brain that may lead to difficulty in walking and problems in balance. A new methodology for hemiplegia gait patterns classification based on bicluster analysis, which aims to identify a group of patients with similar gait patterns, and verify if spatial-temporal gait parameters are correlated with the Barthel Index, has been proposed. Methods: Eighteen hemiplegia patients were recruited. Measurements included spatial-temporal gait parameters and joint moments. Gait data were measured using a motion tracking system and two force platforms. Bicluster analysis was used to classify the subjects' gait patterns. The relation between Barthel Index and spatial-temporal gait parameters was determined based on the Spearman correlation. Results: A high correlation between spatial-temporal gait parameters and Barthel Index (r > .5, p < .05) was observed. Well-separated biclusters presenting similarity among the lower limb joints during the gait cycles were obtained from the data. Conclusions: Bicluster analysis can be useful for identifying patients with similar gait patterns. The relation between the gait patterns and the underlying impairments would allow clinicians to target rehabilitation strategies at the patient’s individual needs.