A Multi-Scale Network with Cross-View Gated Attention Enhancement for Human Activity Recognition

Human Activity Recognition (HAR) based on inertial sensors plays a pivotal role in consumer electronics applications such as health monitoring, fitness tracking, and human-computer interaction. However, achieving robust HAR performance remains challenging due to significant variations in activity duration and rhythms, restricted views in attention modeling, and information loss caused by excessive dimensionality compression. In this paper, we propose MSNCVGA, a multi-scale network enhanced by cross-view gated attention for inertial sensor-based HAR tasks. This method explicitly models sequence temporal scale diversity through multi-scale convolutions and a cross-scale feature fusion branch, while utilizing the cross-view gated attention mechanism to synergistically focus on complementary and dependent cross-view global key features among channel, sequence, and sample dimensions, effectively avoiding excessive information loss. Finally, a recurrent neural network is employed to integrate contextual features from different scale branches. Experiments on five public datasets demonstrate that MSNCVGA achieves better performance than the baseline methods, reaching F1-scores of 0.848, 0.989, 0.985, 0.642, and 0.979 on PAMAP2, UCI HAR, mHealth, RealWorld, and KU-HAR, respectively. Notably, the proposed model has only 0.54M parameters, providing a high-accuracy, low-latency solution suitable for deployment on resource-constrained consumer electronic devices.