Continuous Arousal Self-assessments Validation Using Real-time Physiological Responses

Ting Li, Yoann Baveye, Christel Chamaret, E. Dellandréa, Liming Chen
{"title":"Continuous Arousal Self-assessments Validation Using Real-time Physiological Responses","authors":"Ting Li, Yoann Baveye, Christel Chamaret, E. Dellandréa, Liming Chen","doi":"10.1145/2813524.2813527","DOIUrl":null,"url":null,"abstract":"On one hand, the fact that Galvanic Skin Response (GSR) is highly correlated with the user affective arousal provides the possibility to apply GSR in emotion detection. On the other hand, temporal correlation of real-time GSR and self-assessment of arousal has not been well studied. This paper confronts two modalities representing the induced emotion when watching 30 movies extracted from the LIRIS-ACCEDE database. While continuous arousal annotations have been self-assessed by 5 participants using a joystick, real-time GSR signal of 13 other subjects is supposed to catch user emotional response, objectively without user's interpretation. As a main contribution, this paper introduces a method to make possible the temporal comparison of both signals. Thus, temporal correlation between continuous arousal peaks and GSR were calculated for all 30 movies. A global Pearson's correlation of 0.264 and a Spearman's rank correlation coefficient of 0.336 were achieved. This result proves the validity of using both signals to measure arousal and draws a reliable framework for the analysis of such signals.","PeriodicalId":197562,"journal":{"name":"Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2813524.2813527","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

Abstract

On one hand, the fact that Galvanic Skin Response (GSR) is highly correlated with the user affective arousal provides the possibility to apply GSR in emotion detection. On the other hand, temporal correlation of real-time GSR and self-assessment of arousal has not been well studied. This paper confronts two modalities representing the induced emotion when watching 30 movies extracted from the LIRIS-ACCEDE database. While continuous arousal annotations have been self-assessed by 5 participants using a joystick, real-time GSR signal of 13 other subjects is supposed to catch user emotional response, objectively without user's interpretation. As a main contribution, this paper introduces a method to make possible the temporal comparison of both signals. Thus, temporal correlation between continuous arousal peaks and GSR were calculated for all 30 movies. A global Pearson's correlation of 0.264 and a Spearman's rank correlation coefficient of 0.336 were achieved. This result proves the validity of using both signals to measure arousal and draws a reliable framework for the analysis of such signals.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于实时生理反应的持续觉醒自我评估验证
一方面,皮肤电反应(GSR)与用户情感唤醒高度相关的事实为将GSR应用于情感检测提供了可能。另一方面,实时GSR与觉醒自我评价的时间相关性研究尚未得到很好的研究。本文对从LIRIS-ACCEDE数据库中提取的30部电影的观看过程中产生的情感进行了两种表现形式的研究。虽然连续的唤醒注释是由5名使用操纵杆的参与者自我评估的,但其他13名受试者的实时GSR信号应该是捕捉用户的情绪反应,客观地不需要用户的解释。作为主要贡献,本文介绍了一种方法,使这两个信号的时间比较成为可能。因此,计算了所有30部电影的连续觉醒峰值与GSR之间的时间相关性。全局Pearson相关系数为0.264,Spearman等级相关系数为0.336。这一结果证明了使用这两种信号来测量唤醒的有效性,并为分析这两种信号绘制了一个可靠的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Prediction of User Ratings of Oral Presentations using Label Relations Do Others Perceive You As You Want Them To?: Modeling Personality based on Selfies Session details: Keynote Address - 1 Aesthetic Photo Enhancement using Machine Learning and Case-Based Reasoning Blending Users, Content, and Emotions for Movie Recommendations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1