Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data

Talia Tron; Abraham Peled; Alexander Grinsphoon; Daphna Weinshall

doi:10.1109/BHI.2016.7455874

Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data

Talia Tron, Abraham Peled, Alexander Grinsphoon, Daphna Weinshall

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis, facial activity was characterized in terms of Typicality, Richness and Distribution of 7 facial-clusters. Thus we found patients' facial activity to be poorer, more typical, and characterized mainly by neutral (flat) expressions. The facial features defined in our study achieved up to 85% correct diagnosis classification rate in a SVM based two-step algorithm, and were in significant correlation with Flat Affect severity. Our results demonstrate how the use of assistive technology and data-driven computational tools allow for a comprehensive description of patients' facial behavior in clinical settings, and may contribute to the reliability and accuracy of psychiatric diagnosis.

Original language	English
Title of host publication	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	220-223
Number of pages	4
ISBN (Electronic)	9781509024551
DOIs	https://doi.org/10.1109/BHI.2016.7455874
State	Published - 18 Apr 2016
Event	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States Duration: 24 Feb 2016 → 27 Feb 2016

Publication series

Name	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Conference

Conference	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Country/Territory	United States
City	Las Vegas
Period	24/02/16 → 27/02/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/BHI.2016.7455874

Cite this

Tron, T., Peled, A., Grinsphoon, A., & Weinshall, D. (2016). Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data. In 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 (pp. 220-223). Article 7455874 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2016.7455874

Tron, Talia ; Peled, Abraham ; Grinsphoon, Alexander et al. / Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data. 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 220-223 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016).

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abstract = "One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis, facial activity was characterized in terms of Typicality, Richness and Distribution of 7 facial-clusters. Thus we found patients' facial activity to be poorer, more typical, and characterized mainly by neutral (flat) expressions. The facial features defined in our study achieved up to 85% correct diagnosis classification rate in a SVM based two-step algorithm, and were in significant correlation with Flat Affect severity. Our results demonstrate how the use of assistive technology and data-driven computational tools allow for a comprehensive description of patients' facial behavior in clinical settings, and may contribute to the reliability and accuracy of psychiatric diagnosis.",

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Tron, T, Peled, A, Grinsphoon, A & Weinshall, D 2016, Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data. in 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016., 7455874, 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 220-223, 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, Las Vegas, United States, 24/02/16. https://doi.org/10.1109/BHI.2016.7455874

Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data. / Tron, Talia; Peled, Abraham; Grinsphoon, Alexander et al.
3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 220-223 7455874 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Tron T, Peled A, Grinsphoon A, Weinshall D. Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data. In 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 220-223. 7455874. (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016). doi: 10.1109/BHI.2016.7455874

Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data

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