Clustering the clusters - Knowledge enhancing tool for diagnosing elderly falling risk

• Main Authors: Worasak Rueangsirarak, Anthony S. Atkins, Bernadette Sharp, Nopasit Chakpitak, Komsak Meksamoot, Prapas Pothongsunun
• Format: Journal
• Published: 2018
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84880461676&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/47794
• Institution: Chiang Mai University

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. Copyright © 2013 Inderscience Enterprises Ltd.