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

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Abstract

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. We evaluate the proposed method on two widely used public datasets, UCI-HAR and WISDM, and achieve state-of-the-art performance. Specifically, on the UCI-HAR dataset, our model achieves accuracy, F1-macro, and F1-weighted scores of 93.58%, 93.83%, and 92.16%, respectively. On the WISDM dataset, the scores are 94.28%, 94.01%, and 94.25%, respectively. Our results show that the proposed MAG-Res2Net model can flexibly control time and space costs by adding or reducing network layers, and has better performance compared to existing methods.© 2023 Institute of Physics and Engineering in Medicine.