Evidence map›Paper›PMID 37332873›Full record

ArticleFrontiers in neuroscience2023

Video elicited physiological signal dataset considering indoor temperature factors.

Kunxia Wang, Zihao Zhao, Xueting Shen, Takashi Yamauchi

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kunxia WangSchool of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, China.
Zihao ZhaoSchool of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, China.
Xueting ShenSchool of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, China.
Takashi YamauchiDepartment of Psychological and Brain Sciences, Texas A&M University, College Station, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Human emotions vary with temperature factors. However, most studies on emotion recognition based on physiological signals overlook the influence of temperature factors. This article proposes a video induced physiological signal dataset (VEPT) that considers indoor temperature factors to explore the impact of different indoor temperature factors on emotions. Methods: This database contains skin current response (GSR) data obtained from 25 subjects at three different indoor temperatures. We selected 25 video clips and 3 temperatures (hot, comfortable, and cold) as motivational materials. Using SVM, LSTM, and ACRNN classification methods, sentiment classification is performed on data under three indoor temperatures to analyze the impact of different temperatures on sentiment. Results: The recognition rate of emotion classification under three different indoor temperatures showed that anger and fear had the best recognition effect among the five emotions under hot temperatures, while joy had the worst recognition effect. At a comfortable temperature, joy and calmness have the best recognition effect among the five emotions, while fear and sadness have the worst recognition effect. In cold temperatures, sadness and fear have the best recognition effect among the five emotions, while anger and joy have the worst recognition effect. Discussion: This article uses classification to recognize emotions from physiological signals under the three temperatures mentioned above. By comparing the recognition rates of different emotions at three different temperatures, it was found that positive emotions are enhanced at comfortable temperatures, while negative emotions are enhanced at hot and cold temperatures. The experimental results indicate that there is a certain correlation between indoor temperature and physiological emotions.

Indexed as

classification modelemotion recognitionindoor temperature stimuliphysiological signalsvideo stimulus

Identifiers

PMID37332873
PMCPMC10272375

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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.