Deep learning-based fall detection

Jason Wei Hoe Chiang, Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationDevelopments of Artificial Intelligence Technologies in Computation and Robotics
Subtitle of host publicationProceedings of the 14th International FLINS Conference on Robotics and Artificial Intelligence (FLINS 2020)
EditorsZhong Li, Chunrong Yuan, Jie Lu, Etienne E. Kerre
Place of PublicationSingapore
PublisherWorld Scientific
Pages891-898
Number of pages8
Volume12
ISBN (Electronic)9789811223341, 9789811223334
ISBN (Print)9789811223327
DOIs
Publication statusPublished - Oct 2020
EventThe 14th International FLINS Conference on Robotics and Artificial Intelligence (FLINS 2020) - FernUniversitä t in Hagen/TH Köln, Cologne, Germany
Duration: 18 Aug 202021 Aug 2020
https://www.hrm-bildung.de/flins2020/

Publication series

NameWorld Scientific Proceedings Series on Computer Engineering and Information Science
PublisherWorld Scientific
Volume12
ISSN (Print)1793-7868

Conference

ConferenceThe 14th International FLINS Conference on Robotics and Artificial Intelligence (FLINS 2020)
CountryGermany
CityCologne
Period18/08/2021/08/20
Internet address

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