MCNN-CMCA: A Multiscale Convolutional Neural Networks with Cross-Modal Channel Attention for Physiological Signal-Based Mental State Recognition DOI
Yayun Wei, Lei Cao, Yilin Dong

et al.

Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104856 - 104856

Published: Nov. 7, 2024

Language: Английский

HFA-Net: hierarchical feature aggregation network for micro-expression recognition DOI Creative Commons
Meng Zhang, Wenzhong Yang, Liejun Wang

et al.

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(3)

Published: Feb. 12, 2025

Language: Английский

Citations

0

Uncertainty-Aware Cross Entropy for Robust Learning with Noisy Labels DOI
Lin Wang, Fang Liu, Xiaofen Xing

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0

Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum DOI Open Access
Mateusz Knapik, Bogusław Cyganek,

Tomasz Balon

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3502 - 3502

Published: Sept. 3, 2024

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vibrations, limited computational resources, data privacy concerns, and inherent variability in driver behavior. Analyzing states using visible spectrum imaging particularly challenging under low-light at night. Additionally, relying on single behavioral indicator often fails provide comprehensive assessment driver’s condition. To address these challenges, we propose system that operates exclusively far-infrared spectrum, enabling detection critical features yawning, head drooping, pose estimation regardless scenario. It integrates channel fusion module assess state more accurately underpinned by our custom-developed annotated datasets, along with modified deep neural network designed facial feature thermal spectrum. Furthermore, introduce two modules synthesizing events into coherent state: one based simple machine another combines modality encoder large language model. This latter approach allows generation responses queries beyond system’s explicit training. Experimental evaluations demonstrate high accuracy detecting responding signs fatigue distraction.

Language: Английский

Citations

2

MCNN-CMCA: A Multiscale Convolutional Neural Networks with Cross-Modal Channel Attention for Physiological Signal-Based Mental State Recognition DOI
Yayun Wei, Lei Cao, Yilin Dong

et al.

Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104856 - 104856

Published: Nov. 7, 2024

Language: Английский

Citations

1