A robust optimized convolutional neural network model for human activity recognition using sensing devices

Abstract

Human activity recognition has become an expansive field of interest in recent years, both in academic and industrial research. Human Activity recognition (HAR) is concerning the prediction of person's movement or action such as walking, standing, sitting, up and downstairs, etc. Convolutional neural network (CNN) is a key component with deep learning. The main objective of this study is to design and implement an activity recognition algorithm in the state of the art of deep learning systems which accomplish superlative performance to detect human activities. Accordingly, an extensive comparison has been developed between different deep learning algorithms such as classical (CNN) models and Recurrent Neural Network (RNN) models with respect to the major human activities. Furthermore, a robust (CNN) deep learning model has been built up and proposed in order to enhance the recognition precision of human activities. This proposed model uses raw data acquired from a set of inertial sensor and exploring numerous human achas been built up and proposedtivities; sitting, standing, jogging, walking, and etc. Experimental results show that the precision of the proposed deep structure has achieved 97.5% with respect to the NAdam optimizer which would be considered as the most effectively recognizer compared to other deep learning architectures.