Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals

Author:

Can Yekta Said1ORCID,André Elisabeth2ORCID

Affiliation:

1. Chair for human-centered artificial intelligence, Augsburg University, Germany

2. Human-Centered Multimedia Lab, Augsburg University, Germany

Funder

Deutsche Forschungsgemeinschaft

AI Production Network Augsburg

Bavarian Ministry of Science and Arts

Publisher

ACM

Reference32 articles.

1. 2018. Understanding the stress response. http://www.health.harvard.edu/ accessed at December 2018. 2018. Understanding the stress response. http://www.health.harvard.edu/ accessed at December 2018.

2. 2019. PSS-14. https://das.nh.gov/wellness/docs/percieved%20stress%20scale.pdf accessed at December 2019 . 2019. PSS-14. https://das.nh.gov/wellness/docs/percieved%20stress%20scale.pdf accessed at December 2019.

3. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

4. How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

5. Yekta Said Can and M.  Erdem Kabadayı . 2020. Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers. Applied Sciences 10, 16 ( 2020 ). Yekta Said Can and M. Erdem Kabadayı. 2020. Automatic CNN-Based Arabic Numeral Spotting and Handwritten Digit Recognition by Using Deep Transfer Learning in Ottoman Population Registers. Applied Sciences 10, 16 (2020).

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