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Volume 9
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10.3390/electronics9111843
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Article

Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks

by
Luis Muñoz-Saavedra
1,*,
Francisco Luna-Perejón
1,
Javier Civit-Masot
1,
Lourdes Miró-Amarante
1,2,
Anton Civit
1,2 and
Manuel Domínguez-Morales
1,2
1
Robotics and Technology of Computers Lab, University of Seville, ETSII, 41004 Sevilla, Spain
2
I3US: Research Institute of Computer Engineering, University of Seville, 41004 Sevilla, Spain
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(11), 1843; https://doi.org/10.3390/electronics9111843
Submission received: 28 September 2020 / Revised: 26 October 2020 / Accepted: 31 October 2020 / Published: 3 November 2020
(This article belongs to the Special Issue Applications and Trends on Artificial Intelligence-Based Assistive Technology)
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Abstract

Non-verbal communication is essential in the communication process. This means that its lack can cause misinterpretations of the message that the sender tries to transmit to the receiver. With the rise of video calls, it seems that this problem has been partially solved. However, people with cognitive disorders such as those with some kind of Autism Spectrum Disorder (ASD) are unable to interpret non-verbal communication neither live nor by video call. This work analyzes the relationship between some physiological measures (EEG, ECG, and GSR) and the affective state of the user. To do that, some public datasets are evaluated and used for a multiple Deep Learning (DL) system. Each physiological signal is pre-processed using a feature extraction process after a frequency study with the Discrete Wavelet Transform (DWT), and those coefficients are used as inputs for a single DL classifier focused on that signal. These multiple classifiers (one for each signal) are evaluated independently and their outputs are combined in order to optimize the results and obtain additional information about the most reliable signals for classifying the affective states into three levels: low, middle, and high. The full system is carefully detailed and tested, obtaining promising results (more than 95% accuracy) that demonstrate its viability.
Keywords: deep learning; feature extraction; discrete wavelet transform; affective state; cognitive disorders; autism; ASD; EEG; ECG; GSR deep learning; feature extraction; discrete wavelet transform; affective state; cognitive disorders; autism; ASD; EEG; ECG; GSR

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MDPI and ACS Style

Muñoz-Saavedra, L.; Luna-Perejón, F.; Civit-Masot, J.; Miró-Amarante, L.; Civit, A.; Domínguez-Morales, M. Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks. Electronics 2020, 9, 1843. https://doi.org/10.3390/electronics9111843

AMA Style

Muñoz-Saavedra L, Luna-Perejón F, Civit-Masot J, Miró-Amarante L, Civit A, Domínguez-Morales M. Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks. Electronics. 2020; 9(11):1843. https://doi.org/10.3390/electronics9111843

Chicago/Turabian Style

Muñoz-Saavedra, Luis, Francisco Luna-Perejón, Javier Civit-Masot, Lourdes Miró-Amarante, Anton Civit, and Manuel Domínguez-Morales. 2020. "Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks" Electronics 9, no. 11: 1843. https://doi.org/10.3390/electronics9111843

APA Style

Muñoz-Saavedra, L., Luna-Perejón, F., Civit-Masot, J., Miró-Amarante, L., Civit, A., & Domínguez-Morales, M. (2020). Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks. Electronics, 9(11), 1843. https://doi.org/10.3390/electronics9111843

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