Detect depression from communication: how computer vision, signal processing, and sentiment analysis join forces-Reference-Cited by-同舟云学术

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Detect depression from communication: how computer vision, signal processing, and sentiment analysis join forces

Published:2018-07-03 Issue:3 Volume:8 Page:196-208
ISSN:2472-5579
Container-title:IISE Transactions on Healthcare Systems Engineering
language:en
Short-container-title:IISE Transactions on Healthcare Systems Engineering

Author:

Samareh Aven¹^ORCID,Jin Yan²,Wang Zhangyang³,Chang Xiangyu⁴,Huang Shuai¹

Affiliation:

1. Industrial & Systems Engineering Department, University of Washington, Seattle, Washington, USA;

2. Research Engineer, JD.com, Inc., San francisco, California, USA;

3. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA;

4. School of Management, Xi’an Jiaotong University Shaanxi, P.R. China

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725579.2018.1496494

Reference74 articles.

1. Sex differences in lateral facial facility: The effects of habitual emotional concealment

2. Detecting depression: A comparison between spontaneous and read speech

3. Head Pose and Movement Analysis as an Indicator of Depression

4. Cross-Cultural Depression Recognition from Vocal Biomarkers

5. Reflections of depression in acoustic measures of the patient’s speech

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2. Machine Learning-Based Approaches for Early Prediction of Depression;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

3. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches;Sensors;2024-01-06

4. A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition;IEEE Access;2024

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