GRU-INC: An inception-attention based approach using GRU for human activity recognition

Human Activity Recognition (HAR) is very useful for the clinical applications, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Although handcrafted feature extraction techniques were used in the past, Artificial Neural Network (ANN) is now more popular. In this work, a model has been proposed called Gated Recurrent Unit-Inception (GRU-INC) model has been proposed, which is an Inception-Attention based approach using Gated Recurrent Unit (GRU) that effectively makes use of the temporal and spatial information of the time-series data. The proposed model achieved an F1-score of 96.27%, 90.05%, 90.30%, 99.12%, and 95.99% on the publicly available datasets such as, UCI-HAR, OPPORTUNITY, PAMAP2, WISDM, and Daphnet, respectively. GRU along with Attention Mechanism (AM) was utilized for the temporal part, and Inception module along with Convolutional Block Attention Module (CBAM) was exploited for the spatial part of the model. The proposed architecture was evaluated against state-of-the-art models and similar works. It has been proved that the GRU-INC model has a higher recognition rate as well as lower computational cost. Thus our framework could be applicable in activity associated clinical and rehabilitation applications. •A GRU-Inception based deep learning model to identify human activities.•Attention mechanism is incorporated with GRU to improve temporal feature extraction.•Inception modules along with a CBAM block further highlights the spatial features.•The model is relatively wider rather than a deep structure thus reducing complexity.