Applicability of interactive evolutionary computation to mental health measurement

Hideyuki Takagi; Tomohiro Takahashi; Ken Aoki

doi:10.1109/ICSMC.2004.1401105

Applicability of interactive evolutionary computation to mental health measurement

Hideyuki Takagi
, Tomohiro Takahashi
, Ken Aoki

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

10 Citations (Scopus)

Abstract

We show experimentally the applicability of interactive evolutionary computation (IEC) to a new application field, mental health measurement. We had 3 schizophrenics and 5 mentally healthy students design happy and sad impression computer graphics (CG) lighting images using IEC and asked other 33 students to evaluate the CG images using Scheffé's method of paired comparison. Statistical tests of the evaluations showed that the range of emotional impressions perceived by the three schizophrenics between happy-sad was significantly narrower than that which was perceived by the mentally healthy students (p < 0.01). This experimental result implies that IEC has potential to contribute to psychiatry, and it also showed the possibility to expand IEC applicability beyond conventional system optimization and to a new area, mental health measurement.

Original language	English
Title of host publication	2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Pages	5714-5718
Number of pages	5
DOIs	https://doi.org/10.1109/ICSMC.2004.1401105
Publication status	Published - 2004
Event	2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands Duration: Oct 10 2004 → Oct 13 2004

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	6
ISSN (Print)	1062-922X

Other

Other	2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
Country/Territory	Netherlands
City	The Hague
Period	10/10/04 → 10/13/04

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ICSMC.2004.1401105

Cite this

Applicability of interactive evolutionary computation to mental health measurement. / Takagi, Hideyuki; Takahashi, Tomohiro; Aoki, Ken.
2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004. 2004. p. 5714-5718 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 6).

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

Takagi, H, Takahashi, T & Aoki, K 2004, Applicability of interactive evolutionary computation to mental health measurement. in 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5714-5718, 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004, The Hague, Netherlands, 10/10/04. https://doi.org/10.1109/ICSMC.2004.1401105

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abstract = "We show experimentally the applicability of interactive evolutionary computation (IEC) to a new application field, mental health measurement. We had 3 schizophrenics and 5 mentally healthy students design happy and sad impression computer graphics (CG) lighting images using IEC and asked other 33 students to evaluate the CG images using Scheff{\'e}'s method of paired comparison. Statistical tests of the evaluations showed that the range of emotional impressions perceived by the three schizophrenics between happy-sad was significantly narrower than that which was perceived by the mentally healthy students (p < 0.01). This experimental result implies that IEC has potential to contribute to psychiatry, and it also showed the possibility to expand IEC applicability beyond conventional system optimization and to a new area, mental health measurement.",

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KW - Schizophrenia

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Applicability of interactive evolutionary computation to mental health measurement

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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