Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier

Satya Samyukta Kambhampati; Vishal Singh; M. Sabarimalai Manikandan; Barathram Ramkumar

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier

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Author(s): Satya Samyukta Kambhampati ¹ ; Vishal Singh ¹ ; M. Sabarimalai Manikandan ¹ ; Barathram Ramkumar ¹
- Affiliations: 1: School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, Odisha 751013, India
Source: Volume 2, Issue 4, August 2015, p. 101 – 107
DOI: 10.1049/htl.2015.0018 , Online ISSN 2053-3713

Received 03/04/2015, Accepted 22/06/2015, Revised 22/06/2015, Published 03/08/2015

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%.

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Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier

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