Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2017 Nov 1;17(11):2509.
doi: 10.3390/s17112509.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Ramesh Rajagopalan  1 , Irene Litvan  2 , Tzyy-Ping Jung  3
Affiliations
Review

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Ramesh Rajagopalan et al. Sensors (Basel). .

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.

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

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Interaction between various fall risk factors.
See this image and copyright information in PMC

References

    1. Burns E.R., Stevens J.A., Lee R. The direct costs of fatal and non-fatal falls among older adults—United States. J. Saf. Res. 2016;58:99–103. doi: 10.1016/j.jsr.2016.05.001. - DOI - PMC - PubMed
    1. Scheffer A.C., Schuurmans M.J., Van Dijk N., Van Der Hooft T., De Rooij S.E. Fear of falling: Measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing. 2008;37:19–24. doi: 10.1093/ageing/afm169. - DOI - PubMed
    1. Brownsell S., Hawley M.S. Automatic fall detectors and the fear of falling. J. Telemed. Telecare. 2004;10:262–266. doi: 10.1258/1357633042026251. - DOI - PubMed
    1. Hsieh C.J., Chiang C.Y., Huang C.N., Chan C.T., Hsu S.J. Development of fall characteristics collection system for fall prevention strategies; Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering; Wuhan, China. 10–12 May 2011; pp. 1–4.
    1. Igual R., Medrano C., Plaza I. Challenges, issues and trends in fall detection systems. Biomed. Eng. Online. 2013;12:66. doi: 10.1186/1475-925X-12-66. - DOI - PMC - PubMed