ReviewOpen Access
Deep Learning Based Systems Developed for Fall Detection: A Review
Authors
Author Affiliations
Khulna University of Engineering and Technology, Taibah University, Bangladesh University of Engineering and Technology, Universiti Malaysia Pahang Al-Sultan Abdullah
Published InIEEE Access
Year2020
Citations145
Abstract
Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This article provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN,…
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