Title: Clustering the clusters - knowledge enhancing tool for diagnosing elderly falling risk

Authors: Worasak Rueangsirarak; Anthony S. Atkins; Bernadette Sharp; Nopasit Chakpitak; Komsak Meksamoot; Prapas Pothongsunun

Addresses: Faculty of Computing, Engineering and Sciences, Staffordshire University, Staffordshire, ST18 0AD, UK; College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand; School of Information Technology, Mae Fah Luang University, Chiang Rai, 57100, Thailand ' Faculty of Computing, Engineering and Sciences, Staffordshire University, Staffordshire, ST18 0AD, UK ' Faculty of Computing, Engineering and Sciences, Staffordshire University, Staffordshire, ST18 0AD, UK ' College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand ' College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand ' Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand

Abstract: Falls which affect the musculoskeletal system are the leading cause of injury in people over 65 years. To address the growing problem of falls in an ageing society and to support and improve the healthcare service provided, a diagnostic tool is required. This study proposes a new approach to analyse and diagnose the risks associated with elderly falling by applying K-means clustering to cluster and assess the fall risks data of elderly Thai people, captured using motion capture technology. These clusters are mapped into two-dimensional space using self-organising map (SOM). The resulting 95.45% accuracy suggests that the two-stage clustering technique is applicable and useful in managing fall risks which can then be included in decision support system to assist physiotherapists, in recommending a customised rehabilitation programme.

Keywords: K-means clustering; self-organising maps; SOM; decision support systems; DSS; motion capture technology; elderly; old people; falling risk; two-stage clustering; healthcare technology; falls; ageing society; Thailand; physiotherapists; customised rehabilitation.

DOI: 10.1504/IJHTM.2013.055083

International Journal of Healthcare Technology and Management, 2013 Vol.14 No.1/2, pp.39 - 60

Received: 26 Oct 2011
Accepted: 17 Oct 2012
Published online: 19 Jul 2014 *

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