MAG-Res2Net: a novel deep learning network for human activity recognition

Abstract

Objective. Human activity recognition (HAR) has become increasingly important in healthcare, sports, and fitness domains due to its wide range of applications. However, existing deep learning based HAR methods often overlook the challenges posed by the diversity of human activities and data quality, which can make feature extraction difficult. To address these issues, we propose a new neural network model called MAG-Res2Net, which incorporates the Borderline-SMOTE data upsampling algorithm, a loss function combination algorithm based on metric learning, and the Lion optimization algorithm. Approach. We evaluated the proposed method on two commonly utilized public datasets, UCI-HAR and WISDM, and leveraged the CSL-SHARE multimodal human activity recognition dataset for comparison with state-of-the-art models. Main results. On the UCI-HAR dataset, our model achieved accuracy, F1-macro, and F1-weighted scores of 94.44%, 94.38%, and 94.26%, respectively. On the WISDM dataset, the corresponding scores were 98.32%, 97.26%, and 98.42%, respectively. Significance. The proposed MAG-Res2Net model demonstrates robust multimodal performance, with each module successfully enhancing model capabilities. Additionally, our model surpasses current human activity recognition neural networks on both evaluation metrics and training efficiency. Source code of this work is available at: https://github.com/LHY1007/MAG-Res2Net.