International Journal of Telemedicine and Clinical Practices
These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.
Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.
Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.
International Journal of Telemedicine and Clinical Practices (1 paper in press)
Systematic review of indoor fall detection systems for the elderly using Kinect by Amina BEN HAJ KHALED, A.L.I. KHALFALLAH, Mohamed Salim BOUHLEL Abstract: The fall of the elderly presents a major health problem as it may cause
fatal injuries. To improve the life quality of the elderly, researchers have
developed several fall detection systems. Several sensors have been used to
overcome this problem. So far, Microsoft Kinect has been the most used camerabased sensor for fall detection. This motion detector can interact with computers
through gestures and voice commands.
In this article, we presented a comprehensive survey of the latest fall detection
research using the Kinect sensor. We provide an overview of the main features
of the two Kinect versions V1 and V2 and compare their performances. Then we
detailed the method used for the articles selection.
We provided a classification of the fall detection techniques to highlight the main
differences between them. Finally, we concluded that it is not enough to evaluate
a system performance under simulated conditions. It is important to test these
approaches on old people who are likely to fall.
Keywords: Depth sensor; elderly health care; fall detection; Kinect V1; Kinect
V2; PRISMA; machine learning.