An Efficient Hierarchical Multiscale and Multidimensional Feature Adaptive Fusion Network for Human Activity Recognition Using Wearable Sensors

As the Internet of Things (IoT) technology advances, human activity recognition (HAR) using IoT devices, including wearable sensors has become prevalent in various applications. Nevertheless, many sensor-based HAR methods still struggle to balance recognition accuracy with network complexity. Meanwhile, most existing sensor-based HAR networks fail to achieve an effective fusion of multidimensional features. To address the above issues, a hierarchical multiscale time-frequency and channel feature adaptive fusion (HMTF-CFAF) network is put forward. The HMTF-CFAF efficiently extracts unique multiscale time-frequency and channel features in sensor data using hierarchical connectivity. Furthermore, it incorporates a feature fusion mechanism to integrate and exchange multiscale and multidimensional features, providing more comprehensive and richer features. To evaluate the HMTF-CFAF network, we utilize three datasets: 1) the University of California Irvine HAR (UCI-HAR); 2) physical activity monitoring for aging people (PAMAP2); and 3) self-collected household behavior (HB) dataset. The HMTF-CFAF network achieves the accuracies of 97.66%, 98.75%, and 98.80% on the above three datasets, respectively, demonstrating its excellent performance.