警惕(心理学)
计算机科学
残余物
人工智能
特征提取
脑电图
注意力网络
模式识别(心理学)
保险丝(电气)
机器学习
工程类
心理学
算法
神经科学
精神科
电气工程
生物
作者
Jiahui Pan,Xugang Cai,Danying Mo,Yong Yu,Yuanqing Li
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-12
标识
DOI:10.1109/tim.2023.3307756
摘要
Driver vigilance estimation is essential for fatigue and traffic accident reduction. Although previous algorithms for driver vigilance estimation have achieved high evaluation performance, several challenges remain open: 1) How to effectively utilize multimodal electroencephalography (EEG) and electrooculogram (EOG) signals remains challenging. 2) How to capture vector feature information in part-to-whole space more effectively. 3) How to enhance the core ability of a network, including in-depth mining of features and eliminating redundant information as much as possible. To address these challenges, we propose a novel multimodal detection method for driver vigilance estimation, which consists of residual attention blocks and a capsule attention mechanism. Specifically, we propose a residual attention network to optimize the channel-space features of low-level capsules to adaptively rescale the features of each channel to fuse the interdependence among feature channels. Meanwhile, the part-whole relationship in the features is explored with the use of dynamic routing, the features with large contributions are strengthened, and the transmission efficiency of the neural network is ultimately improved. The experimental datasets show that the residual attention capsule network (Res-att-capsnet) outperforms the state-of-the-art baselines in both regression and classification tasks. The feasibility and effectiveness of our proposed method are thus demonstrated.
科研通智能强力驱动
Strongly Powered by AbleSci AI