@article{ref1,
title="Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier",
journal="Healthcare technology letters",
year="2015",
author="Kambhampati, Satya Samyukta and Singh, Vishal and Manikandan, M. Sabarimalai and Ramkumar, Barathram",
volume="2",
number="4",
pages="101-107",
abstract="In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.<p /> <p>Language: en</p>",
language="en",
issn="2053-3713",
doi="10.1049/htl.2015.0018",
url="http://dx.doi.org/10.1049/htl.2015.0018"
}