Deep-Learning Domain Adaptation to Improve Generalizability across Subjects and Contexts in Detecting Construction Workers’ Stress from Biosignals-Reference-Cited by-同舟云学术

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Deep-Learning Domain Adaptation to Improve Generalizability across Subjects and Contexts in Detecting Construction Workers’ Stress from Biosignals

Published:2024-05 Issue:3 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Lee Gaang¹^ORCID,Lee SangHyun²

Affiliation:

1. Assistant Professor, Hole School of Construction Engineering and Management, Dept. of Civil and Environmental Engineering, Univ. of Alberta, Donadeo Innovation Centre for Engineering, 9211-116 St., Edmonton, AB, Canada T6G2H5. ORCID: .

2. Professor, Tishman Construction Management Program, Dept. of Civil and Environmental Engineering, Univ. of Michigan, 2350 Hayward St., G.G Brown Bldg., Ann Arbor, MI 48109 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5665

Reference65 articles.

1. Modeling the relationship between occupational stressors, psychosocial/physical symptoms and injuries in the construction industry

2. Analysis of Representations for Domain Adaptation

3. Hypocretin/orexin in arousal and stress

4. Bianco S. P. Napoletano and R. Schettini. 2019. “Multimodal car driver stress recognition.” In Proc. 13th EAI Int. Conf. on Pervasive Computing Technologies for Healthcare 302–307. New York: Association for Computing Machinery.

5. Methods and approaches on inferring human emotional stress changes through physiological signals: A review;Bong S. Z.;Int. J. Med. Eng. Inf.,2013

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