DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition

With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning algorithms have been proposed for HAR problems in th...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 11; s. 15321 - 15331
Hlavní autoři: Zhu, Yida, Luo, Haiyong, Chen, Runze, Zhao, Fang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2024
Témata:
Adaptation models
Algorithms
Attention
Convolutional denoising autoencoders (CDAEs)
Convolutional neural networks
Correlation
Deep Learning
Feature extraction
global-attention mechanism
Graph convolutional networks
Human Activities - classification
Human activity recognition
human activity recognition (HAR)
Humans
multisensor modality
Multisensor systems
Neural Networks, Computer
Noise reduction
Pattern Recognition, Automated - methods
self-attention mechanism
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning algorithms have been proposed for HAR problems in the past decades, these methods have limited capability to exploit semantic features from multiple sensor types. To address this limitation, we propose a novel HAR framework, DiamondNet, which can create heterogeneous multisensor modalities, denoise, extract, and fuse features from a fresh perspective. In DiamondNet, we leverage multiple 1-D convolutional denoising autoencoders (1-D-CDAEs) to extract robust encoder features. We further introduce an attention-based graph convolutional network to construct new heterogeneous multisensor modalities, which adaptively exploit the potential relationship between different sensors. Moreover, the proposed attentive fusion subnet, which jointly employs a global-attention mechanism and shallow features, effectively calibrates different-level features of multiple sensor modalities. This approach amplifies informative features and provides a comprehensive and robust perception for HAR. The efficacy of the DiamondNet framework is validated on three public datasets. The experimental results demonstrate that our proposed DiamondNet outperforms other state-of-the-art baselines, achieving remarkable and consistent accuracy improvements. Overall, our work introduces a new perspective on HAR, leveraging the power of multiple sensor modalities and attention mechanisms to significantly improve the performance.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3285547