Detecting emotional reactions to videos of depression

Xuanying Zhu; Tom Gedeon; Sabrina Caldwell; Richard Jones

doi:10.1109/INES46365.2019.9109519

Detecting emotional reactions to videos of depression

Xuanying Zhu, Tom Gedeon, Sabrina Caldwell, Richard Jones

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

8 Citations (Scopus)

Abstract

We can physically sense depression in others, and this recognition can be detected by using neural networks to analyse our physiological responses to observing individuals with depression. The behaviour of 16 individuals suffering from various levels depression were shown in short videos to 12 experiment participants (observers) whose physiological signals we recorded. Consciously, depression is not normally interesting, so does not provoke strong conscious recognition, and hence barely over chance, at 27%. However, at emotional levels, depression is interesting, so provokes physiological reactions we can measure, leading to neural network classification of 92%.

Original language	English
Title of host publication	INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	147-152
Number of pages	6
ISBN (Electronic)	9781728112138
DOIs	https://doi.org/10.1109/INES46365.2019.9109519
Publication status	Published - Apr 2019
Event	23rd IEEE International Conference on Intelligent Engineering Systems, INES 2019 - Godollo, Hungary Duration: 25 Apr 2019 → 27 Apr 2019

Publication series

Name	INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings

Conference

Conference	23rd IEEE International Conference on Intelligent Engineering Systems, INES 2019
Country/Territory	Hungary
City	Godollo
Period	25/04/19 → 27/04/19

Access to Document

10.1109/INES46365.2019.9109519

Cite this

Zhu, X., Gedeon, T., Caldwell, S., & Jones, R. (2019). Detecting emotional reactions to videos of depression. In INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings (pp. 147-152). Article 9109519 (INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INES46365.2019.9109519

Zhu, Xuanying ; Gedeon, Tom ; Caldwell, Sabrina et al. / Detecting emotional reactions to videos of depression. INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 147-152 (INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings).

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title = "Detecting emotional reactions to videos of depression",

abstract = "We can physically sense depression in others, and this recognition can be detected by using neural networks to analyse our physiological responses to observing individuals with depression. The behaviour of 16 individuals suffering from various levels depression were shown in short videos to 12 experiment participants (observers) whose physiological signals we recorded. Consciously, depression is not normally interesting, so does not provoke strong conscious recognition, and hence barely over chance, at 27%. However, at emotional levels, depression is interesting, so provokes physiological reactions we can measure, leading to neural network classification of 92%.",

keywords = "Depression Detection, Galvanic Skin Response, Physiological Signals, Pupil Dilation, Skin Temperature",

author = "Xuanying Zhu and Tom Gedeon and Sabrina Caldwell and Richard Jones",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 23rd IEEE International Conference on Intelligent Engineering Systems, INES 2019 ; Conference date: 25-04-2019 Through 27-04-2019",

year = "2019",

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doi = "10.1109/INES46365.2019.9109519",

language = "English",

series = "INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "147--152",

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address = "United States",

}

Zhu, X, Gedeon, T, Caldwell, S & Jones, R 2019, Detecting emotional reactions to videos of depression. in INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings., 9109519, INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 147-152, 23rd IEEE International Conference on Intelligent Engineering Systems, INES 2019, Godollo, Hungary, 25/04/19. https://doi.org/10.1109/INES46365.2019.9109519

Detecting emotional reactions to videos of depression. / Zhu, Xuanying; Gedeon, Tom; Caldwell, Sabrina et al.
INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 147-152 9109519 (INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings).

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

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N2 - We can physically sense depression in others, and this recognition can be detected by using neural networks to analyse our physiological responses to observing individuals with depression. The behaviour of 16 individuals suffering from various levels depression were shown in short videos to 12 experiment participants (observers) whose physiological signals we recorded. Consciously, depression is not normally interesting, so does not provoke strong conscious recognition, and hence barely over chance, at 27%. However, at emotional levels, depression is interesting, so provokes physiological reactions we can measure, leading to neural network classification of 92%.

AB - We can physically sense depression in others, and this recognition can be detected by using neural networks to analyse our physiological responses to observing individuals with depression. The behaviour of 16 individuals suffering from various levels depression were shown in short videos to 12 experiment participants (observers) whose physiological signals we recorded. Consciously, depression is not normally interesting, so does not provoke strong conscious recognition, and hence barely over chance, at 27%. However, at emotional levels, depression is interesting, so provokes physiological reactions we can measure, leading to neural network classification of 92%.

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KW - Physiological Signals

KW - Pupil Dilation

KW - Skin Temperature

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M3 - Conference contribution

T3 - INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings

SP - 147

EP - 152

BT - INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd IEEE International Conference on Intelligent Engineering Systems, INES 2019

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ER -

Zhu X, Gedeon T, Caldwell S, Jones R. Detecting emotional reactions to videos of depression. In INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 147-152. 9109519. (INES 2019 - IEEE 23rd International Conference on Intelligent Engineering Systems, Proceedings). doi: 10.1109/INES46365.2019.9109519

Detecting emotional reactions to videos of depression

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