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Sensors 2017, 17(11), 2509; https://doi.org/10.3390/s17112509

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

1
School of Engineering, University of St. Thomas, St. Paul, MN 55105, USA
2
Department of Neurosciences, University of California, San Diego, CA 92093, USA
3
Institute for Neural Computation, University of California, San Diego, CA 92093, USA
*
Author to whom correspondence should be addressed.
Received: 25 August 2017 / Revised: 25 October 2017 / Accepted: 27 October 2017 / Published: 1 November 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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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. View Full-Text
Keywords: fall prediction; fall prevention; internet of things; information fusion; wearable and ambient sensing fall prediction; fall prevention; internet of things; information fusion; wearable and ambient sensing
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Rajagopalan, R.; Litvan, I.; Jung, T.-P. Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions. Sensors 2017, 17, 2509.

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