Deep learning-based fall detection

Jason Wei Hoe  Chiang; Li Zhang

doi:10.1142/9789811223334_0107

Deep learning-based fall detection

Jason Wei Hoe Chiang, Li Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring.

Original language	English
Title of host publication	Deep learning-based fall detection
Place of Publication	In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.
Publisher	World Scientific, Singapore
Pages	891-898
Number of pages	8
DOIs	https://doi.org/10.1142/9789811223334_0107
Publication status	Published - 15 Aug 2020

Access to Document

10.1142/9789811223334_0107

Cite this

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title = "Deep learning-based fall detection",

abstract = "In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring.",

author = "Chiang, {Jason Wei Hoe} and Li Zhang",

year = "2020",

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publisher = "World Scientific, Singapore",

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Deep learning-based fall detection. / Chiang, Jason Wei Hoe ; Zhang, Li.
Deep learning-based fall detection . In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.: World Scientific, Singapore, 2020. p. 891-898.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring.

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