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An Efficient Hierarchical Multiscale and Multidimensional Feature Adaptive Fusion Network for Human Activity Recognition Using Wearable Sensors | IEEE Journals & Magazine | IEEE Xplore

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


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 More

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)
Page(s): 6492 - 6505
Date of Publication: 05 November 2024

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