Human Activity Recognition in Smart Home With Deep Learning Approach

Abstract

Vision-based human activity recognition in smart homes has become a significant issue in terms of developing the next generation technologies which can improve healthcare and security of smart homes. Recently, deep learning models that aim to automatic extraction of low-level to high-level features of input data instead of using complicated conventional feature extraction methods have achieved significant improvements in the classification of a large amount of data especially vision-based datasets. Therefore, in this study in order to recognize human action of a smart home video dataset (DMLSmartActions) Convolutional Neural Networks (CNNs) architecture as a deep learning model has been proposed. Moreover, the performance of the proposed method has been compared with the previous methods which have used traditional machine learning methods on the same dataset. Experimental results demonstrated that the proposed deep learning model has achieved 82.41% accuracy rate in the classification of human activity.