Triaxial Accelerometer-Based Fall Detection Method Using a Self-Constructing Cascade-AdaBoost-SVM Classifier
- 4 January 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 17 (2), 411-419
- https://doi.org/10.1109/jbhi.2012.2237034
Abstract
In this paper, we propose a cascade-AdaBoost-support vector machine (SVM) classifier to complete the triaxial accelerometer-based fall detection method. The method uses the acceleration signals of daily activities of volunteers from a database and calculates feature values. By taking the feature values of a sliding window as an input vector, the cascade-AdaBoost-SVM algorithm can self-construct based on training vectors, and the AdaBoost algorithm of each layer can automatically select several optimal weak classifiers to form a strong classifier, which accelerates effectively the processing speed in the testing phase, requiring only selected features rather than all features. In addition, the algorithm can automatically determine whether to replace the AdaBoost classifier by support vector machine. We used the UCI database for the experiment, in which the triaxial accelerometers are, respectively, worn around the left and right ankles, and on the chest as well as the waist. The results are compared to those of the neural network, support vector machine, and the cascade-AdaBoost classifier. The experimental results show that the triaxial accelerometers around the chest and waist produce optimal results, and our proposed method has the highest accuracy rate and detection rate as well as the lowest false alarm rate.Keywords
This publication has 41 references indexed in Scilit:
- Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted ActivitiesSensors, 2012
- Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activitiesJournal of Biomechanics, 2010
- Using Accelerometers for Physical Actions Recognition by a Neural Fuzzy NetworkTelemedicine and e-Health, 2009
- A comparison of automatic fall detection by the cross-product and magnitude of tri-axial accelerationPhysiological Measurement, 2009
- Activity identification using body-mounted sensors—a review of classification techniquesPhysiological Measurement, 2009
- User-Based Motion Sensing and Fuzzy Logic for Automated Fall Detection in Older AdultsTelemedicine and e-Health, 2007
- Evaluation of a threshold-based tri-axial accelerometer fall detection algorithmGait & Posture, 2007
- Pyroelectric IR sensor arrays for fall detection in the older populationJournal de Physique IV, 2005
- Falls, Injuries Due to Falls, and the Risk of Admission to a Nursing HomeThe New England Journal of Medicine, 1997
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997