Exploring Conventional, Automated and Deep Machine Learning for Electrodermal Activity-Based Drivers’ Stress Recognition-Reference-Cited by-同舟云学术

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Exploring Conventional, Automated and Deep Machine Learning for Electrodermal Activity-Based Drivers’ Stress Recognition

Published:2021-10-17 Issue: Volume: Page:
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Container-title:2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Lingelbach Katharina,Bui Michael,Diederichs Frederik,Vukelic Mathias

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9658572/9658575/09658662.pdf?arnumber=9658662

Reference36 articles.

1. Scikit-learn: Machine Learning in Python;pedregosa;Journal of Machine Learning Research,2011

2. Multi-modal behavioral biometrics based on hci and electrophysiology;gamboa;PhD thesis,2008

3. cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing

4. Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study

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