A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms

Nabil Zerrouki, Fouzi Harrou, Ying Sun, Amina Zouina Ait Djafer, Houacine Amrane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

As reported by numerous World Health Organization studies, fall incidents are considered one of the leading causes of loss of autonomy, injuries, and even deaths, and this is not only among elderly people but also in other categories such as workers. Fall incidents also have a considerable impact on the budget allocated to the care of people suffering from the effects of falls. This work presents a comprehensive review of state-of-the-art fall detection technologies considering the most powerful machine learning methodologies, both classical formalism (shallow methods), and approaches based on deep learning formalism. The authors reviewed the most recent and effective methods for fall detection and presented the used sensors, cameras, applied pre-treatments, generated attributes, and algorithms used in this field of application. The present work is completed by a discussion presenting some limitations that need to be analyzed and taken into account to further improve the quality of fall detection and reduce their impacts.
Original languageEnglish (US)
Title of host publication2022 7th International Conference on Frontiers of Signal Processing (ICFSP)
PublisherIEEE
ISBN (Print)978-1-6654-8159-5
DOIs
StatePublished - Oct 28 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-02

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