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Applied Computational Intelligence and Soft Computing
Volume 2018, Article ID 5434897, 15 pages
Research Article

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

1School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
2Faculty of Science and Technology, Rajamangala University of Technology Krungthep, Bangkok, Thailand
3Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand

Correspondence should be addressed to Bunthit Watanapa;

Received 20 October 2017; Revised 26 January 2018; Accepted 12 March 2018; Published 3 June 2018

Academic Editor: Xiaohui Yuan

Copyright © 2018 Bunthit Watanapa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.