Classification of Driver Cognitive Load based on Physiological Data: Exploring Recurrent Neural Networks-Reference-Cited by-同舟云学术

Classification of Driver Cognitive Load based on Physiological Data: Exploring Recurrent Neural Networks

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Advanced Robotics and Mechatronics (ICARM)
language:
Short-container-title:

Author:

Kumar Shekhar¹,He Dengbo²,Qiao Guangkai¹,Donmez Birsen¹

Affiliation:

1. Univeristy of Toronto,Toronto,ON,Canada,M5S 3 G8

2. Systems Hub, the Hong Kong University of Science and Technology (Guangzhou),Guangzhou,China

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9959088/9959097/09959588.pdf?arnumber=9959588

Reference31 articles.

1. Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

2. Using Wearable Devices for Non-invasive, Inexpensive Physiological Data Collection

3. Cognitive Analysis of Working Memory Load from Eeg, by a Deep Recurrent Neural Network

4. Driver mental workload classification through the use of physiological data;he;Digital Signal Processing and Machine Learning for Interactive Systems Developers,2021

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Computational Models for In-Vehicle User Interface Design: A Systematic Literature Review;Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications;2024-09-11

2. Driving Attention State Detection Based on GRU-EEGNet;Sensors;2024-08-07

3. Rethinking the Evaluation of Driver Behavior Analysis Approaches;IEEE Transactions on Intelligent Transportation Systems;2024-08

4. Cognitive Workload Estimation in Conditionally Automated Vehicles Using Transformer Networks Based on Physiological Signals;Transportation Research Record: Journal of the Transportation Research Board;2024-06-10

5. Evaluating the robustness of multimodal task load estimation models;Frontiers in Computer Science;2024-04-10