Literature DB >> 26295056

Automated Audiovisual Depression Analysis.

Jeffrey M Girard1, Jeffrey F Cohn1.   

Abstract

Analysis of observable behavior in depression primarily relies on subjective measures. New computational approaches make possible automated audiovisual measurement of behaviors that humans struggle to quantify (e.g., movement velocity and voice inflection). These tools have the potential to improve screening and diagnosis, identify new behavioral indicators of depression, measure response to clinical intervention, and test clinical theories about underlying mechanisms. Highlights include a study that measured the temporal coordination of vocal tract and facial movements, a study that predicted which adolescents would go on to develop depression based on their voice qualities, and a study that tested the behavioral predictions of clinical theories using automated measures of facial actions and head motion.

Entities:  

Year:  2015        PMID: 26295056      PMCID: PMC4539261          DOI: 10.1016/j.copsyc.2014.12.010

Source DB:  PubMed          Journal:  Curr Opin Psychol        ISSN: 2352-250X


  5 in total

1.  Linking "big" personality traits to anxiety, depressive, and substance use disorders: a meta-analysis.

Authors:  Roman Kotov; Wakiza Gamez; Frank Schmidt; David Watson
Journal:  Psychol Bull       Date:  2010-09       Impact factor: 17.737

2.  Multichannel weighted speech classification system for prediction of major depression in adolescents.

Authors:  Kuan Ee Brian Ooi; Margaret Lech; Nicholas B Allen
Journal:  IEEE Trans Biomed Eng       Date:  2012-11-21       Impact factor: 4.538

3.  Nonverbal Social Withdrawal in Depression: Evidence from manual and automatic analysis.

Authors:  Jeffrey M Girard; Jeffrey F Cohn; Mohammad H Mahoor; S Mohammad Mavadati; Zakia Hammal; Dean P Rosenwald
Journal:  Image Vis Comput       Date:  2014-10       Impact factor: 2.818

4.  Facing Imbalanced Data Recommendations for the Use of Performance Metrics.

Authors:  László A Jeni; Jeffrey F Cohn; Fernando De La Torre
Journal:  Int Conf Affect Comput Intell Interact Workshops       Date:  2013

5.  Spontaneous facial expression in unscripted social interactions can be measured automatically.

Authors:  Jeffrey M Girard; Jeffrey F Cohn; Laszlo A Jeni; Michael A Sayette; Fernando De la Torre
Journal:  Behav Res Methods       Date:  2015-12
  5 in total
  8 in total

1.  Editorial overview: The assessment, etiology, and treatment of unipolar depression.

Authors:  Christopher G Beevers
Journal:  Curr Opin Psychol       Date:  2015-08-01

2.  Depression Severity Assessment for Adolescents at High Risk of Mental Disorders.

Authors:  Michal Muszynski; Jamie Zelazny; Jeffrey M Girard; Louis-Philippe Morency
Journal:  Proc ACM Int Conf Multimodal Interact       Date:  2020-10

3.  Disclosing Critical Voice Features for Discriminating between Depression and Insomnia-A Preliminary Study for Developing a Quantitative Method.

Authors:  Ray F Lin; Ting-Kai Leung; Yung-Ping Liu; Kai-Rong Hu
Journal:  Healthcare (Basel)       Date:  2022-05-18

4.  Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews.

Authors:  Shalini Bhatia; Roland Goecke; Zakia Hammal; Jeffrey F Cohn
Journal:  Proc Int Conf Autom Face Gesture Recognit       Date:  2019-07-11

5.  The Simple Video Coder: A free tool for efficiently coding social video data.

Authors:  Daniel Barto; Clark W Bird; Derek A Hamilton; Brandi C Fink
Journal:  Behav Res Methods       Date:  2017-08

6.  A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health.

Authors:  Xin Chen; Zhigeng Pan
Journal:  Int J Environ Res Public Health       Date:  2021-06-14       Impact factor: 3.390

7.  Head movements and postures as pain behavior.

Authors:  Philipp Werner; Ayoub Al-Hamadi; Kerstin Limbrecht-Ecklundt; Steffen Walter; Harald C Traue
Journal:  PLoS One       Date:  2018-02-14       Impact factor: 3.240

8.  Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development.

Authors:  Michael L Birnbaum; Avner Abrami; John M Kane; Guillermo Cecchi; Stephen Heisig; Asra Ali; Elizabeth Arenare; Carla Agurto; Nathaniel Lu
Journal:  JMIR Ment Health       Date:  2022-01-24
  8 in total

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