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-theart 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.

Automated summary

Human Activity Recognition (HAR) is a valuable research field for clinical applications, where machine learning algorithms play a significant role. The proposed Gated Recurrent Unit-Inception (GRU-INC) model effectively utilizes both temporal and spatial information of time-series data, achieving high F1-scores on various publicly available datasets. The combination of GRU with Attention Mechanism and Inception module with Convolutional Block Attention Module (CBAM) contributes to the superior recognition rate and lower computational cost of the GRU-INC model. This framework has the potential to be applied in activity-associated clinical and rehabilitation applications.