Evidence map›Paper›PMID 29958457›Full record

ReviewSensors (Basel, Switzerland)2018

A Review of Emotion Recognition Using Physiological Signals.

Lin Shu, Jinyan Xie, Mingyue Yang, Ziyi Li, Zhenqi Li, Dan Liao, Xiangmin Xu, Xinyi Yang

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
152citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

152 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Digital Horizons: Enhancing Autism Support with Augmented Reality.Journal of autism and developmental disorders · 2026
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92 more citing papers are in PubMed but not listed here.

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

8 authors.

Lin ShuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. shul@scut.edu.cn.
Jinyan XieSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. xie.jinyan@mail.scut.edu.cn.
Mingyue YangSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. eemingyueyang@mail.scut.edu.cn.
Ziyi LiSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. eve.llzzyy@gmail.com.
Zhenqi LiSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. li.zhenqi@mail.scut.edu.cn.
Dan LiaoSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. danl@scut.edu.cn.
Xiangmin XuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. xmxu@scut.edu.cn.
Xinyi YangSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China. cindy.yang1001@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

Indexed as

Recognition, PsychologyEmotional IntelligenceEmotionsHumansModels, Psychologicalclassifiersemotion modelemotion recognitionemotion stimulationfeaturesphysiological signals

Identifiers

PMID29958457
PMCPMC6069143

What OpenQuestion holds

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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.