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Deep Learning Based Systems Developed for Fall Detection: A Review
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DOI：10.1109/ACCESS.2020.3021943.png)
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摘要
En 中文
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, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.
Keyword:
Machine learning
Sensors
Senior citizens
Injuries
Convolutional neural networks
Statistics
Auto-encoder
convolutional neural network
deep learning
fall detection
long short-term memory
recurrent convolutional network
recurrent neural network
review
surveillance system
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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