Automatic Human Fall Detection Using Multiple Tri-axial Accelerometers

Fouzi Harrou, Nabil Zerrouki, Abdelkader Dairi, Ying Sun, Amrane Houacine

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

Abstract

Accurately detecting human falls of elderly people at an early stage is vital for providing early alert and avoid serious injury. Towards this purpose, multiple triaxial accelerometers data has been used to uncover falls based on an unsupervised monitoring procedure. Specifically, this paper introduces a one-class support vector machine (OCSVM) scheme into human fall detection. The main motivation behind the use of OCSVM is that it is a distribution-free learning model and can separate nonlinear features in an unsupervised way need for labeled data. The proposed OCSVM scheme was evaluated on fall detection databases from the University of Rzeszow's. Three other promising classification algorithms, Mean shift, Expectation-Maximization, k-means, were also assessed based on the same datasets. Their detection performances were compared with those obtained by the OCSVM algorithm. The results showed that the OCSVM scheme outperformed the other methods.
Original languageEnglish (US)
Title of host publication2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
PublisherIEEE
Pages74-78
Number of pages5
ISBN (Print)9781665440325
DOIs
StatePublished - Sep 29 2021

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