Abstract:
As the Internet of Things (IoT) technology advances, human activity recognition (HAR) using IoT devices, including wearable sensors has become prevalent in various applic...Show MoreMetadata
Abstract:
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.
Published in: IEEE Internet of Things Journal ( Volume: 12, Issue: 6, 15 March 2025)