Open AccessUnified framework for triaxial accelerometer‐based fall event detection and classification using cumulants and hierarchical decision tree classifier
Author(s) -
Kambhampati Satya Samyukta,
Singh Vishal,
Manikandan M. Sabarimalai,
Ramkumar Barathram
Publication year - 2015
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2015.0018
Subject(s) - support vector machine , pattern recognition (psychology) , naive bayes classifier , artificial intelligence , cumulant , decision tree , computer science , classifier (uml) , accelerometer , higher order statistics , mathematics , machine learning , signal processing , statistics , radar , telecommunications , operating system
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%.