Mining gait pattern for clinical locomotion diagnosis based on clustering techniques

Guandong XU, Yanchun ZHANG, Rezaul BEGG

Research output: Chapter in Book/Report/Conference proceedingChapters

13 Citations (Scopus)

Abstract

Scientific gait (walking) analysis provides valuable information about an individual's locomotion function, in turn, to assist clinical diagnosis and prevention, such as assessing treatment for patients with impaired postural control and detecting risk of falls in elderly population. While several artificial intelligence (AI) paradigms are addressed for gait analysis, they usually utilize supervised techniques where subject groups are defined a priori. In this paper, we explore to investigate gait pattern mining with clustering-based approaches, in which k-means and hierarchical clustering algorithms are employed to derive gait pattern. After feature selection and data preparation, we conduct clustering on the constructed gait data model to build up pattern-based clusters. The centroids of clusters are then treated as the subject profiles to model the various kinds of gait pattern, e.g. normal or pathological. Experiments are undertaken to visualize the derived subject clusters, evaluate the quality of clustering paradigm in terms of silhouette and mean square error and compare the results with the discovery derived from hierarchy tree analysis. In addition, analysis conducted on test data demonstrates the usability of the proposed paradigm in clinical applications. Copyright © 2006 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationAdvanced data mining and applications: Second International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, proceedings
EditorsXue LI, Osmar R. ZAÏANE, Zhanhuai LI
Place of PublicationBerlin
PublisherSpringer
Pages296-307
ISBN (Electronic)9783540370260
ISBN (Print)9783540370253
DOIs
Publication statusPublished - 2006

Citation

Xu, G., Zhang, Y., & Begg, R. (2006). Mining gait pattern for clinical locomotion diagnosis based on clustering techniques. In X. Li, O. R. Zaïane, & Z. Li (Eds.), Advanced data mining and applications: Second International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, proceedings (pp. 296-307). Springer. https://doi.org/10.1007/11811305_33

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