Evidence map›Paper›PMID 36553887›Full record

ArticleHealthcare (Basel, Switzerland)2022

Comparison of Subjective Facial Emotion Recognition and "Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection" between Patients with Schizophrenia and Healthy Participants.

Toshiya Akiyama, Kazuyuki Matsumoto, Kyoko Osaka, Ryuichi Tanioka, Feni Betriana, Yueren Zhao, Yoshihiro Kai, Misao Miyagawa, Yuko Yasuhara, Hirokazu Ito and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 7 institutions in 2 countries.

Toshiya AkiyamaGraduate School of Health Sciences, Tokushima University, Tokushima 770-8509, Japan.
Kazuyuki MatsumotoGraduate School of Engineering, Tokushima University, Tokushima 770-8506, Japan.ORCID 0000-0002-9820-1470
Kyoko OsakaDepartment of Psychiatric Nursing, Nursing Course of Kochi Medical School, Kochi University, Kochi 783-8505, Japan.
Ryuichi TaniokaDepartment of Physical Therapy, Hiroshima Cosmopolitan University, Hiroshima 734-0014, Japan.
Feni BetrianaRozzano Locsin Institute, Tokushima 770-8509, Japan.
Yueren ZhaoDepartment of Psychiatry, Fujita Health University, Nagoya 470-1192, Japan.
Yoshihiro KaiDepartment of Mechanical Engineering, Tokai University, Tokyo 151-8677, Japan.ORCID 0000-0002-6155-3704
Misao MiyagawaDepartment of Nursing, Faculty of Health and Welfare, Tokushima Bunri University, Tokushima 770-8514, Japan.
Yuko YasuharaInstitute of Biomedical Sciences, Tokushima University, Tokushima 770-8509, Japan.
Hirokazu ItoInstitute of Biomedical Sciences, Tokushima University, Tokushima 770-8509, Japan.
Gil SorianoDepartment of Nursing, College of Allied Health, National University Philippines, Manila 1008, Philippines.ORCID 0000-0002-6349-5560
Tetsuya TaniokaInstitute of Biomedical Sciences, Tokushima University, Tokushima 770-8509, Japan.ORCID 0000-0002-2847-1862
Tokushima University · JPFujita Health University · JPHiroshima Cosmopolitan University · JPKōchi University · JPNational University · PHTokai University · JPTokushima Bunri University · JP

Funding

Center for Design-based AI Education and Research, University of Tokushima N/AJapan Society for the Promotion of Science JP17H01609
6 · The paper itself

Abstract

Patients with schizophrenia may exhibit a flat affect and poor facial expressions. This study aimed to compare subjective facial emotion recognition (FER) and FER based on multi-task cascaded convolutional network (MTCNN) face detection in 31 patients with schizophrenia (patient group) and 40 healthy participants (healthy participant group). A Pepper Robot was used to converse with the 71 aforementioned participants; these conversations were recorded on video. Subjective FER (assigned by medical experts based on video recordings) and FER based on MTCNN face detection was used to understand facial expressions during conversations. This study confirmed the discriminant accuracy of the FER based on MTCNN face detection. The analysis of the smiles of healthy participants revealed that the kappa coefficients of subjective FER (by six examiners) and FER based on MTCNN face detection concurred (κ = 0.63). The perfect agreement rate between the subjective FER (by three medical experts) and FER based on MTCNN face detection in the patient, and healthy participant groups were analyzed using Fisher's exact probability test where no significant difference was observed (

Indexed as

facial emotion recognitionhealthy participantshuman–robot interactionmulti-task cascaded convolutional networkspatients with schizophreniareliability

Identifiers

PMID36553887
PMCPMC9777528
OpenAlexW4310036492

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.