Data mining: concepts and techniques
Data mining: concepts and techniques
Modern Information Retrieval
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Application of data mining techniques on EMG registers of hemiplegic patients
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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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 defineda 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.