Automatic Human Fall Detection Using Multiple Tri-axial Accelerometers
KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Permanent link to this recordhttp://hdl.handle.net/10754/673829
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AbstractAccurately 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.
CitationHarrou, F., Zerrouki, N., Dairi, A., Sun, Y., & Houacine, A. (2021). Automatic Human Fall Detection Using Multiple Tri-axial Accelerometers. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). doi:10.1109/3ict53449.2021.9581558
SponsorsThe work is supported by King Abdullah University of Science and Technology (KAUST) office of sponsored research (OSR) under Award no. OSR-2019-CRG7-3800.
Conference/Event name2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021