Document Type
Article
Publication Date
10-22-2014
Keywords
machine learning, kinect, environment awareness, mobile applications
Digital Object Identifier (DOI)
http://dx.doi.org/10.3390/s141019806
Abstract
According to nihseniorhealth.gov (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.
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Sensors, v. 14, issue 10, p. 19806-19842
Scholar Commons Citation
Santiago Delahoz, Yueng and Angel Labrador, Miguel, "Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors" (2014). Computer Science and Engineering Faculty Publications. 3.
https://digitalcommons.usf.edu/esb_facpub/3
