Author/Authors :
Watanapa, Bunthit School of Information Technology - King Mongkut’s University of Technology Thonburi, Bangkok, Thailand , Patsadu, Orasa Faculty of Science and Technology - Rajamangala University of Technology Krungthep, Bangkok, Thailand , Dajpratham, Piyapat Faculty of Medicine, Siriraj Hospital - Mahidol University, Bangkok, Thailand , Nukoolkit, Chakarida School of Information Technology - King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Abstract :
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 TVset). 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.
