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

[摘要] 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%.

[学科分类] 肠胃与肝脏病学

[关键词] accelerometers;acceleration measurement;decision trees;support vector machines;signal classification;medical signal processing;feature extraction;biomedical measurement;body sensor networks;triaxial accelerometer-based fall event detection;hierarchical decision tree classifier;cumulant extraction;acceleration signals;single waist-mounted triaxial accelerometer;ACC signals;fifth-order cumulants;supports vector machine;fall event classification algorithm;human activity classification;second-order cumulants;naive Bayes;multilayer perceptron;SVM classifiers;time-domain features;third-order cumulants;fourth-order cumulants;optimal detection;lowest false alarm rate