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Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

Department of Computer Science and Engineering, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA
Author to whom correspondence should be addressed.
Sensors 2014, 14(10), 19806-19842;
Received: 18 September 2014 / Revised: 10 October 2014 / Accepted: 16 October 2014 / Published: 22 October 2014
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
PDF [396 KB, uploaded 22 October 2014]


According to (a website for older adults), falling represents a great threat as people get older, and providing mechanisms to detect and prevent falls is critical to improve people’s lives. Over 1.6 million U.S. adults are treated for fall-related injuries in emergency rooms every year suffering fractures, loss of independence, and even death. It is clear then, that this problem must be addressed in a prompt manner, and the use of pervasive computing plays a key role to achieve this. Fall detection (FD) and fall prevention (FP) are research areas that have been active for over a decade, and they both strive for improving people’s lives through the use of pervasive computing. This paper surveys the state of the art in FD and FP systems, including qualitative comparisons among various studies. It aims to serve as a point of reference for future research on the mentioned systems. A general description of FD and FP systems is provided, including the different types of sensors used in both approaches. Challenges and current solutions are presented and described in great detail. A 3-level taxonomy associated with the risk factors of a fall is proposed. Finally, cutting edge FD and FP systems are thoroughly reviewed and qualitatively compared, in terms of design issues and other parameters. View Full-Text
Keywords: machine learning; kinect; environment awareness; mobile applications machine learning; kinect; environment awareness; mobile applications
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 (CC BY 4.0).

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Delahoz, Y.S.; Labrador, M.A. Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors. Sensors 2014, 14, 19806-19842.

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