Human Activity Recognition Using Deep Residual Convolutional Network Based on Wearable Sensors

Human activity recognition (HAR) can play a vital role in biomedical and health informatics by enabling the monitoring of human daily activities and health behaviors. Accurate HAR can provide valuable insights into patients' physical activity levels, and this helps to manage chronic conditions and promote healthy lifestyles. In this paper, we propose a deep learning model, DKInception, designed for HAR tasks. DKInception integrates deep convolutional residual networks with an attention mechanism and leverages multi-scale convolution kernels to efficiently extract temporal features for activity identification. This model is built on the Inception ResNet architecture and extends its capabilities with effective, fast convergence and robust scaling properties. To evaluate the performance of DKInception, we conduct extensive experiments on four benchmark HAR datasets: UCI-HAR, Opportunity, Daphnet, and PAMAP2. Our comparative analysis with several existing models shows that DKInception outperforms these models using various evaluation metrics. These results demonstrate the model recorded high accuracy of 95.70%, 87.48%, 94.00% and 89.72%, for UCI-HAR, Opportunity, Daphnet, and PAMAP2, respectively.