Citation

Rajagopalan R, Litvan I, Jung TP. Sensors (Basel) 2017; 17(11): s17112509.

Affiliation

Institute for Neural Computation, University of California, San Diego, CA 92093, USA. jung@sccn.ucsd.edu.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17112509

PMID

29104256

Abstract

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Language: en

Keywords

fall prediction; fall prevention; information fusion; internet of things; wearable and ambient sensing