The International Arab Journal of Information Technology (IAJIT)


Fall Motion Detection with Fall Severity Level Estimation by Mining Kinect 3D Data Stream

This paper proposes an integrative model of fall motion detection and fall severity level estimation. For the fall motion detection, a continuous stream of data representing time sequential frames of fifteen body joint positions was obtained from Kinect’s 3D depth camera. A set of features is then extracted and fed into the designated machine learning model. Compared with existing models that rely on the depth image inputs, the proposed scheme resolves background ambiguity of the human body. The experimental results demonstrated that the proposed fall detection method achieved accuracy of 99.97% with zero false negative and more robust when compared with the state-of-the-art approach using depth of image. Another key novelty of our approach is the framework, called Fall Severity Injury Score (FSIS), for determining the severity level of falls as a surrogate for seriousness of injury on three selected risk areas of body: head, hip and knee. The framework is based on two crucial pieces of information from the fall: 1) the velocity of the impact position and 2) the kinetic energy of the fall impact. Our proposed method is beneficial to caregivers, nurses or doctors, in giving first aid/diagnosis/treatment for the subject, especially, in cases where the subject loses consciousness or is unable to respond.

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[47] Yu X., Approaches and Principles of Fall Detection for Elderly and Patient, in Proceedings of the 10th International Conference on E-health Networking, Applications and Services, Singapore, pp. 42-47, 2008. Orasa Patsadu received her B.B.A. degree in Computer Information Systems from Rajamangala University of Technology Krungthep, Thailand, in 2007 and M.Sc. in Software Engineering from King Mongkut's University of Technology Thonburi, Thailand in 2010. Since April 2011, she has been pursuing the Ph.D. in School of Information Technology, King Mongkut's University of Technology Thonburi. Her research interests include data mining and software engineering. Bunthit Watanapa received his B.Eng. in Computer Engineering from King Mongkut's Institute of Technology Ladkrabang, Thailand, in 1987 and M.Eng. and Ph.D. in Industrial Engineering from Asian Institute of Technology (AIT), Thailand, in 1990 and 2003, respectively. Currently he is the chairperson of the Business Information System program at the School of Information Technology, King Mongkut's University of Technology Thonburi, Thailand. His research interests are in the area of Decision Support System, Optimization, and Project Management. Piyapat Dajpratham received her M.D. from Mahidol University, Bangkok, Thailand in 1992 and Diplomat Thai Board of Rehabilitation Medicine from Faculty of Medicine Siriraj Hospital in 1997. She had fellowship training in Neurorehabilitation & Geriatric Rehabilitation from Northwestern Healthcare Network, University of Melbourne, Australia in 1998 and 1999 respectively. Currently she is an Associate Professor in Rehabilitation Medicine, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand. Her research interests are stroke rehabilitation and falling in the elderly. Chakarida Nukoolkit received her B.Sc. in Computer Science from Thammasat Univerisity, Thailand in 1992 and M.Sc. in Computer Science from Vanderbilt University, U.S.A. in 1995 and Ph.D. in Computer Science from University of Alabama, U.S.A. in 2001. Previously she had led the Data and Knowledge Engineering Lab at the School of Information Technology, King Mongkut's University of Technology Thonburi, Thailand. Her current research interests are in the area of data mining, data science, visualizations, creative computing, bioinformatics, and artificial intelligence.