Work

Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor

Year: 2018

Type: article

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 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.

Source: Applied Computational Intelligence and Soft Computing

DOI: 10.1155/2018/5434897

Authors: Bunthit Watanapa, Orasa Patsadu, Piyapat Dajpratham, Chakarida Nukoolkit

Institutions: King Mongkut's University of Technology Thonburi, Rajamangala University of Technology Krungthep, Siriraj Hospital, Mahidol University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,000