Stress Detection of Children With ASD Using Physiological Signals

Sevgi Nur Bilgin Aktas; Pinar Uluer; Buket Coskun; Elif Toprak; Duygun Erol Barkana; Hatice Kose; Tatjana Zorcec; Ben Robins; Agnieszka Landowska

doi:10.1109/SIU55565.2022.9864668

Stress Detection of Children With ASD Using Physiological Signals

Sevgi Nur Bilgin Aktas, Pinar Uluer, Buket Coskun, Elif Toprak, Duygun Erol Barkana, Hatice Kose, Tatjana Zorcec, Ben Robins, Agnieszka Landowska

Department of Artificial Intelligence and Data Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

Translated title of the contribution	Fizyolojik Sinyaller Kullanilarak Otizmli Çocuklarda Stres Tanima
Original language	English
Title of host publication	2022 30th Signal Processing and Communications Applications Conference, SIU 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665450928
DOIs	https://doi.org/10.1109/SIU55565.2022.9864668
Publication status	Published - 2022
Event	30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey Duration: 15 May 2022 → 18 May 2022

Publication series

Name	2022 30th Signal Processing and Communications Applications Conference, SIU 2022

Conference

Conference	30th Signal Processing and Communications Applications Conference, SIU 2022
Country/Territory	Turkey
City	Safranbolu
Period	15/05/22 → 18/05/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

This work was supported by the European Commission’s Erasmus+ Project (EMBOA, Affective loop in socially assistive robotics as an intervention tool for children with autism) under Contract 2019-1-PL01-KA203-065096. The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Funders	Funder number
European Commission	2019-1-PL01-KA203-065096

Keywords

autism
child-robot interaction
physiological signals
stress

Access to Document

10.1109/SIU55565.2022.9864668

Cite this

Aktas, S. N. B., Uluer, P., Coskun, B., Toprak, E., Barkana, D. E., Kose, H., Zorcec, T., Robins, B., & Landowska, A. (2022). Stress Detection of Children With ASD Using Physiological Signals. In 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 (2022 30th Signal Processing and Communications Applications Conference, SIU 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU55565.2022.9864668

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title = "Stress Detection of Children With ASD Using Physiological Signals",

abstract = "This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.",

keywords = "autism, child-robot interaction, physiological signals, stress",

author = "Aktas, {Sevgi Nur Bilgin} and Pinar Uluer and Buket Coskun and Elif Toprak and Barkana, {Duygun Erol} and Hatice Kose and Tatjana Zorcec and Ben Robins and Agnieszka Landowska",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 30th Signal Processing and Communications Applications Conference, SIU 2022 ; Conference date: 15-05-2022 Through 18-05-2022",

year = "2022",

doi = "10.1109/SIU55565.2022.9864668",

language = "English",

series = "2022 30th Signal Processing and Communications Applications Conference, SIU 2022",

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Aktas, SNB, Uluer, P, Coskun, B, Toprak, E, Barkana, DE, Kose, H, Zorcec, T, Robins, B & Landowska, A 2022, Stress Detection of Children With ASD Using Physiological Signals. in 2022 30th Signal Processing and Communications Applications Conference, SIU 2022. 2022 30th Signal Processing and Communications Applications Conference, SIU 2022, Institute of Electrical and Electronics Engineers Inc., 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Turkey, 15/05/22. https://doi.org/10.1109/SIU55565.2022.9864668

Stress Detection of Children With ASD Using Physiological Signals. / Aktas, Sevgi Nur Bilgin; Uluer, Pinar; Coskun, Buket et al.
2022 30th Signal Processing and Communications Applications Conference, SIU 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 30th Signal Processing and Communications Applications Conference, SIU 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Stress Detection of Children With ASD Using Physiological Signals

AU - Aktas, Sevgi Nur Bilgin

AU - Uluer, Pinar

AU - Coskun, Buket

AU - Toprak, Elif

AU - Barkana, Duygun Erol

AU - Kose, Hatice

AU - Zorcec, Tatjana

AU - Robins, Ben

AU - Landowska, Agnieszka

PY - 2022

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N2 - This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

AB - This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

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M3 - Conference contribution

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T3 - 2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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Aktas SNB, Uluer P, Coskun B, Toprak E, Barkana DE, Kose H et al. Stress Detection of Children With ASD Using Physiological Signals. In 2022 30th Signal Processing and Communications Applications Conference, SIU 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 30th Signal Processing and Communications Applications Conference, SIU 2022). doi: 10.1109/SIU55565.2022.9864668

Stress Detection of Children With ASD Using Physiological Signals

Abstract

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