Resident activity recognition in smart homes by using artificial neural networks

Abstract

Recognition and detection of human activity is one of the challenges in smart home technologies. In this paper, three algorithms of artificial neural networks, namely Quick Propagation (QP), Levenberg Marquardt (LM) and Batch Back Propagation (BBP), have been used for human activity recognition and compared according to performance on Massachusetts Institute of Technology (MIT) smart home dataset. The achieved results demonstrated that Levenberg Marquardt algorithm has better human activity recognition performance (by 92.81% accuracy) than Quick Propagation and Batch Back Propagation algorithms.