Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)

Implicit Measurement-Based ASD Dynamic Material Emotion Perception Assessment Method

Authors
Yuqing Wang1, Fengshuo Shi1, Shengzhou Li1, Xiaoguan Kang1, Qiongfang Wu2, Xie Li1, *
1Dongguan University of Technology, Dongguan, China
2Dongguan Rehabilitation laboratory school, Dongguan, China
*Corresponding author. Email: xiel@dgut.edu.cn
Corresponding Author
Xie Li
Available Online 18 December 2023.
DOI
10.2991/978-2-38476-158-6_22How to use a DOI?
Keywords
children with autism; emotional perception; Dermatomy; ECG signals; OLS linear regression
Abstract

As the basis of emotional communication and social interaction, emotional perception is a good indicator for the development of individual social skills. In this paper, a dual-modal emotion perception evaluation method based on electrical skin signal (EDA) and electrocardiogram signal (ECG) is proposed to provide more accurate intervention and treatment methods for children with autism (ASD), and at the same time propose the feasibility of multidimensional research on emotions from the physiological perspective. Methods Through the emotion perception experiment, the electrodermal signal and ECG signal of the subject were synchronously collected based on implicit measurement, the relevant features of the event were extracted, and the baseline correction, normalization and normality tests were carried out, and the dynamic material emotion perception evaluation dataset was formed together with the rehabilitation teacher evaluation score, and the ASD emotion perception evaluation model was constructed by the least squares method (OLS). Results Based on the SCMean, TonicMean features, SDNN, PowerNormalized, and SD2 features of electrodermal signals, the OLS linear regression perception evaluation model can realize the mapping of physiological indicators to emotional perception evaluation (coefficient of determination of electrodermal signal model R2=0.994; coefficient of determination of ECG signal model R2=0.970). At the same time, it also shows that OLS can combine the physiological indicators of electrodermal and ECG signals to better assess the emotional perception of ASD.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
18 December 2023
ISBN
10.2991/978-2-38476-158-6_22
ISSN
2352-5398
DOI
10.2991/978-2-38476-158-6_22How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Yuqing Wang
AU  - Fengshuo Shi
AU  - Shengzhou Li
AU  - Xiaoguan Kang
AU  - Qiongfang Wu
AU  - Xie Li
PY  - 2023
DA  - 2023/12/18
TI  - Implicit Measurement-Based ASD Dynamic Material Emotion Perception Assessment Method
BT  - Proceedings of the 2023 International Conference on Applied Psychology and Modern Education (ICAPME 2023)
PB  - Atlantis Press
SP  - 156
EP  - 167
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-158-6_22
DO  - 10.2991/978-2-38476-158-6_22
ID  - Wang2023
ER  -