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.
Highlights
Falls are a major cause of injuries, with over one-third of older adults experiencing at least one fall or more each year [1]
There is a critical need for the development of cost-effective fall prediction systems to reduce the financial and health burdens associated with the consequences of a fall
Limitations, challenges, and future research directions for designing internet of things (IoT)-enabled fall prediction systems
Summary
Falls are a major cause of injuries, with over one-third of older adults experiencing at least one fall or more each year [1]. These reviews mainly focus on falls in the context of wearable sensors, gait analysis, assistive devices, signal processing, and machine learning algorithms [5,6,7,8] They do not address the challenges inherent in the multifactorial nature of falls, extrinsic fall risk factors, user-centric design principles, and performance analysis in real life conditions on frequent fallers. Fall prediction systems focus on information fusion from both wearable and ambient sensors for reliable estimation of fall risk These systems include the design and evaluation of user interfaces such as smartphone applications for fall prevention intervention and educating subjects on fall risk factors. We present recommendations for overcoming these limitations and describe key focus areas for future research
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