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A Two-Tier Approach for Fall Detection Systems

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Publicado en:Journal of Computer Science and Control Systems vol. 15, no. 2 (Oct 2022), p. 5
Autor principal: Akintotimi, Akinyemi Omololu
Otros Autores: Oladele, Rufus Olalere
Publicado:
University of Oradea
Materias:
Datasets
Fall detection
Algorithms
Smartphones
Older people
Machine learning
Support vector machines
False alarms
Kernel functions
Sensors
Activities of daily living
Acceso en línea:Citation/Abstract
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Resumen:- A critical performance drawback of most fall detection systems is high false alarms. These false alarms are due to the imbalanced mix of the "fall" and "non-fall" data contained in the processed datasets on one hand, and the inherent limitation of the processing algorithms, on the other hand. To tackle this false alarm problem, a two-tier solution approach which entails Synthetic Minority Over-Sampling Technique (SMOTE) and hybrid of two machine learning algorithms (Multiple-Kernel Support Vector Machine (MK-SVM) and Multinomial Naive Bayes (MNB), hereafter known as SMOTE-based MKSVM-MNB is proposed. The results of simulation experiments performed using two open-source datasets namely SisFall Dataset and UMAFall Dataset show that SMOTE-based MKSVM-MNB significantly outperforms MKSVM, MNB and MKSVM-MNB in terms of the number of False Negatives (FN) recorded. Also, MKSVM-MNB significantly outperforms MKSVM and MNB in terms of FN.
ISSN:1844-6043
2067-2101
1841-7213
Fuente:Advanced Technologies & Aerospace Database