Citation

Kambhampati SS, Singh V, Manikandan MS, Ramkumar B. Healthc. Technol. Lett. 2015; 2(4): 101-107.

Affiliation

School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.

Copyright

(Copyright © 2015, Institution of Engineering and Technology)

DOI

10.1049/htl.2015.0018

PMID

26609414

PMCID

PMC4612541

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%.

Language: en