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Human state recognition is a critical topic with pervasive and important applications in human–machine systems. Multimodal fusion, which entails integrating metrics from various data sources, has proven to be a potent method for boosting recognition performance. Although recent multimodal-based models have shown promising results, they often fall short in fully leveraging sophisticated fusion strategies essential for modeling adequate cross-modal dependencies in the fusion representation. Instead, they rely on costly and inconsistent feature crafting and alignment. To address this limitation, we propose an end-to-end multimodal transformer framework for multimodal human state recognition called Husformer. Specifically, we propose using cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive load datasets [multimodal dataset for objective cognitive workload assessment on simultaneous tasks (MOCAS) and CogLoad] demonstrate that in the recognition of the human state, our Husformer outperforms both state-of-the-art multimodal baselines and the use of a single modality by a large margin, especially when dealing with raw multimodal features. We also conducted an ablation study to show the benefits of each component in Husformer. Experimental details and source code are available at https://github.com/SMARTlab-Purdue/Husformer.
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Cross-modal autoencoder framework learns holistic representations of cardiovascular state
Abstract A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.
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- Award ID(s):
- 1651995
- PAR ID:
- 10409844
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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