Risk Clustering for Diagnosing the Falling Risks in Elderly People Using Self-Organizing Map and Motion Capture Technology

A Self-Organizing Map was used to classify the risk level of falling based on the criteria of a Risk Assessment Matrix in order to assess the risk in the elderly. This screening system adopts input data collected from elderly Thai people, using Motion Capture Technology. The classification of the screening system based on the result of SOM validation in this study showed 80% accuracy which suggest that the clustering technique is adaptable and useful in falling risk management.

[1]  A. Atkins,et al.  Application of CBR techniques in elderly falling risk for physiotherapist assessment and support , 2011, 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings.

[2]  M. Painho,et al.  The Self-Organizing Map and it’s variants as tools for geodemographical data analysis: the case of Lisbon’s Metropolitan Area , 2004 .

[3]  Political Dataset Application of Self-organizing Maps to a Political Dataset , 2004 .

[4]  May Yuan,et al.  Community health assessment using self-organizing maps and geographic information systems , 2008, International Journal of Health Geographics.

[5]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[6]  René E.D. Ferdinands ADVANCED APPLICATIONS OF MOTION ANALYSIS IN SPORTS BIOMECHANICS , 2010 .

[7]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[8]  A J van den Bogert,et al.  Response time is more important than walking speed for the ability of older adults to avoid a fall after a trip. , 2002, Journal of biomechanics.

[9]  Yanchun Zhang,et al.  Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping , 2009, 2009 Digital Image Computing: Techniques and Applications.

[10]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[11]  Parag Kulkarni,et al.  Self Organizing Maps to Build Intrusion Detection System , 2010 .

[12]  David E. Booth,et al.  A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms , 2005, Expert Syst. Appl..

[13]  Guojin Zhu,et al.  The Growing Self-organizing Map for Clustering Algorithms in Programming Codes , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[14]  Lisa R. Welsh Preventing falls from unpredictable balance disturbances , 2006 .

[15]  Janet Balas,et al.  Books online , 1993 .

[16]  Liang Gao,et al.  An expert system using rough sets theory and self-organizing maps to design space exploration of complex products , 2010, Expert Syst. Appl..