Content area
- 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.