Sensor Based Human Activity Recognition Using Adaboost Ensemble Classifier

Abstract

Providing accurate information about human activity is an important task in a smart city environment. Human activity is complex, and it is important to use the best technology and benefit from the machine learning to learn about human activity. Although people have been interested in the past decade in recording human activities, there are still major aspects to be addressed to take advantage of technology in the knowledge of human activity. In this paper, Adaboost ensemble classifier is used to recognize human activity data taken from body sensors. Ensemble classifiers achieve better performance by using a weighted combination of several classifier models. Many researchers have shown the efficiencies of ensemble classifiers in different real-world problems. Experimental results have shown the feasibility of Adaboost ensemble classifiers by achieving the better performance for automated human activity recognition by using human body sensors. Results have shown that ensemble classifiers based on Adaboost algorithm significantly improve the performance of automated human activity recognition (HAR).