Deep learning-based fall detection

Hoe Chiang, Jason Wei and Zhang, Li (2020) 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, pp. 891-898. ISBN 9789811223327, 9789811223341, 9789811223334

flins2020_falldetection.pdf - Accepted Version

Download (658kB) | Preview
Official URL:


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.

Item Type: Book Section
Uncontrolled Keywords: Fall Detection, Deep Learning, Convolutional Neural Network
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 29 Sep 2020 08:22
Last Modified: 13 Aug 2021 03:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics