Pilot Stress Detection Through Physiological Signals Using a Transformer-Based Deep Learning Model-Reference-Cited by-同舟云学术

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Pilot Stress Detection Through Physiological Signals Using a Transformer-Based Deep Learning Model

Published:2023-06-01 Issue:11 Volume:23 Page:11774-11784
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Yuhan¹,Li Ke¹^ORCID,Chen Jiaao¹^ORCID,Wang Shaofan¹,Lu Haochang²,Wen Dongsheng³

Affiliation:

1. School of Aeronautic Science and Engineering, Beihang University, Beijing, China

2. School of Computer Science and Technology, Capital University of Economics and Business, Beijing, China

3. Lehrstuhl für Thermodynamik, Technical University of Munich, Munich, Germany

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10139852/10054557.pdf?arnumber=10054557

Reference42 articles.

1. Cardiologist-level arrhythmia detection with convolutional neural networks;rajpurkar;arXiv 1707 01836,2017

2. Adam, a method for stochastic optimization;kingma;arXiv 1412 6980,2014

3. Driver stress detection via multimodal fusion using attention-based CNN-LSTM

4. Transformer-based spatial–temporal feature learning for EEG decoding;song;arXiv 2106 11170,2021

5. Lightweight End-to-End Stress Recognition using Binarized CNN-LSTM Models

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2. A spatiotemporal separable graph convolutional network for oddball paradigm classification under different cognitive-load scenarios;Expert Systems with Applications;2024-09

3. TAGformer: A Multimodal Physiological Signals Fusion Network for Pilot Stress Recognition;IEEE Sensors Journal;2024-07-01

4. A Cross-Stage Partial Network and a Cross-Attention-Based Transformer for an Electrocardiogram-Based Cardiovascular Disease Decision System;Bioengineering;2024-05-29

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