iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

Abstract

Advances in deep learning (DL) model design have pushed the boundaries of the areas in which it can be applied. The fields with an immense availability of complex big data have been big beneficiaries of these advances. One such field is human activity recognition (HAR). HAR is a popular area of research in a connected world because internet-of-things (IoT) devices and smartphones are becoming more prevalent. A major research goal of recent research work has been to improve predictive accuracy for devices with limited computational resources. In this paper, we propose iSPLInception, a DL model motivated by the Inception-ResNet architecture from Google, that not only achieves high predictive accuracy but also uses fewer device resources. We evaluate the proposed model’s performance on four public HAR datasets from the University of California, Irvine (UCI) machine learning repository. The proposed model’s performance is compared to that of existing DL architectures that have been proposed in the recent past to solve the HAR problem. The proposed model outperforms these approaches on several metrics of accuracy, cross-entropy loss, and $F_{1}$ score on all the four datasets. The performance of the proposed iSPLInception model is validated on the UCI HAR using smartphones dataset, Opportunity activity recognition dataset, Daphnet freezing of gait dataset, and PAMAP2 physical activity monitoring dataset. The experiments and result analysis indicate that the proposed iSPLInception model achieves remarkable performance for HAR applications.