Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities

The recognition of human activities has become a dominant emerging research problem and widely covered application areas in surveillance, wellness management, healthcare, and many more. In real life, the activity recognition is a challenging issue because human beings are often performing the activities not only simple but also complex and heterogeneous in nature. Most of the existing approaches are addressing the problem of recognizing only simple straightforward activities (e.g. walking, running, standing, sitting, etc.). Recognizing the complex and heterogeneous human activities are a challenging research problem whereas only a limited number of existing works are addressing this issue. In this paper, we proposed a novel Deep-HAR model by ensembling the Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for recognizing the simple, complex, and heterogeneous type activities. Here, the CNNs are used for extracting the features whereas RNNs are used for finding the useful patterns in time-series sequential data. The activities recognition performance of the proposed model was evaluated using three different publicly available datasets, namely WISDM, PAMAP2, and KU-HAR. Through extensive experiments, we have demonstrated that the proposed model performs well in recognizing all types of activities and has achieved an accuracy of 99.98%, 99.64%, and 99.98% for simple, complex, and heterogeneous activities respectively.