Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors
By Yueng Santiago Delahoz, Miguel A. Labrador. Italian National Conference on Sensors, 2014
339 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/9e4e8a07d61d1015ceb649c39c3a983c4db43059
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Computer Vision and Pattern Recognition
Citations in the corpus
- WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings (2015, background): “Other research has focused on fall detection based on accelerometers using wearable sensors [19–24].”