Novel Human Activity Recognition and Recommendation Models for Maintaining Good Health of Mobile Users

Abstract

With the continuous improvement of the living standard, people have changed their concept from disease treatment to health management. However, most of the current health management software makes recommendations based on users’ static information, with low updating frequency. The effect of targeted suggestions becomes weak with time, and it is hard for the recommendation effect to be satisfactory. Based on the use of smartphones for recognizing human activities in real-time, firstly, a novel 'CNN+GRU' model is proposed in this paper, utilizing both convolutional neural networks (CNNs) and gated recurrent units (GRUs). 'CNN+GRU' can improve the recognition speed and extract the features in sensor data more accurately by achieving in the conducted experiments an average accuracy of 91.27%, thus outperforming other models compared. Secondly, another model, named SimilRec, is proposed for physical activity recommendation to users based on their health profile, the similarities between their current physical activity sequence, and the historical physical activity sequence of other (similar) users.