A Performance Comparision Of Modeling Physical Human Activities
Authors: Thien Kae Jack and Mohd Hanafi Ahmad Hijazi
This paper presents the background of human activity recognition (HAR) using wireless sensors network (WSN) data. Performing HAR using WSN data is an important and challenging task which it may contribute in many domains’ application. Time series classification (TSC) based approach is proposed in this paper to achieve the goal mentioned just now. Datasets that will be used in this research can be acquired from the internet which the dataset was collected for past study. There are six activities performed by the volunteers which are walking, walking upstairs, walking downstairs, sitting, standing, and laying. The TSC approach employs the instance based k-NN with different similarity measure which includes Dynamic Time Warping to perform classification of HAR. Furthermore, other classification approaches were also performed to compare the performance. The involved classifiers are J48 decision tree and Support Vector Machine. Besides using original acquired dataset to perform classification, discretization and feature selection will be applied to the dataset before the classification process. Overall, k-NN with Dynamic Time Warping produced a comparable performance with other classifiers.