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Clustering of Physiological Signals by Emotional State, Race, and Sex

Authors:

Shaun CanavanAuthors Info & Claims

ICMI '21 Companion: Companion Publication of the 2021 International Conference on Multimodal Interaction

Pages 312 - 316

https://doi.org/10.1145/3461615.3486567

Published: 17 December 2021 Publication History

Abstract

In this work, we explore the emotional responses to ten stimuli reflecting real-world experiences captured via physiological signals from 140 individuals. We employ the DBSCAN clustering algorithm to these data, and show that blood pressure and electrodermal activity may be indicative of race, and blood pressure of sex and emotional state. These findings could lead to important innovations, particularly those valuable for certain demographic groups, including, for example, culturally relevant robotics and cultural awareness in education by improving real-time measurements of stress and cognitive load.

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Cited By

Zohdi HNatale LScholkmann FWolf U(2022)Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine LearningBrain Sciences10.3390/brainsci1211144912:11(1449)Online publication date: 27-Oct-2022
https://doi.org/10.3390/brainsci12111449
Kunc D(2022)Unsupervised learning for physiological signals in real-life emotion recognition using wearables2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW57231.2022.10086004(1-5)Online publication date: 18-Oct-2022
https://doi.org/10.1109/ACIIW57231.2022.10086004
Zanna KSridhar KYu HSano A(2022)Bias Reducing Multitask Learning on Mental Health Prediction2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)10.1109/ACII55700.2022.9953850(1-8)Online publication date: 18-Oct-2022
https://doi.org/10.1109/ACII55700.2022.9953850

Index Terms

Clustering of Physiological Signals by Emotional State, Race, and Sex
1. Applied computing
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICMI '21 Companion: Companion Publication of the 2021 International Conference on Multimodal Interaction

October 2021

418 pages

ISBN:9781450384711

DOI:10.1145/3461615

Editors:
Zakia Hammal
Carnegie Mellon University
,
Carlos Busso
University of Texas at Dallas
,
Catherine Pelachaud
CNRS - ISIR, Sorbonne University
,
Sharon Oviatt
Monash University
,
Albert Ali Salah
Utrecht University and Boğaziçi University
,
Guoying Zhao
University of Oulu

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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New York, NY, United States

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Published: 17 December 2021

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ICMI '21

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ICMI '21: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 18 - 22, 2021

QC, Montreal, Canada

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Cited By

Zohdi HNatale LScholkmann FWolf U(2022)Intersubject Variability in Cerebrovascular Hemodynamics and Systemic Physiology during a Verbal Fluency Task under Colored Light Exposure: Clustering of Subjects by Unsupervised Machine LearningBrain Sciences10.3390/brainsci1211144912:11(1449)Online publication date: 27-Oct-2022
https://doi.org/10.3390/brainsci12111449
Kunc D(2022)Unsupervised learning for physiological signals in real-life emotion recognition using wearables2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW57231.2022.10086004(1-5)Online publication date: 18-Oct-2022
https://doi.org/10.1109/ACIIW57231.2022.10086004
Zanna KSridhar KYu HSano A(2022)Bias Reducing Multitask Learning on Mental Health Prediction2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII)10.1109/ACII55700.2022.9953850(1-8)Online publication date: 18-Oct-2022
https://doi.org/10.1109/ACII55700.2022.9953850

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