title: Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.
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%.
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: Satya Samyukta
authors.initials: SS
authors.lastname: Kambhampati
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: Vishal
authors.initials: V
authors.lastname: Singh
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: M Sabarimalai
authors.initials: MS
authors.lastname: Manikandan
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: Barathram
authors.initials: B
authors.lastname: Ramkumar
keywords: ACC signals; SVM classifiers; acceleration measurement; acceleration signals; accelerometers; biomedical measurement; body sensor networks; cumulant extraction; decision trees; fall event classification algorithm; feature extraction; fifth-order cumulants; fourth-order cumulants; hierarchical decision tree classifier; human activity classification; lowest false alarm rate; medical signal processing; multilayer perceptron; naive Bayes; optimal detection; second-order cumulants; signal classification; single waist-mounted triaxial accelerometer; support vector machines; supports vector machine; third-order cumulants; time-domain features; triaxial accelerometer-based fall event detection