ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
J. Adrian López
Universidad de Guadalajara
0009-0004-2523-9680
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Ricardo E. García Manzo
Universidad de Guadalajara
0000-0002-1210-3459
Acerca de
Given the inherent variability of human emotional responses, their objective measurement remains a complex challenge. Emotions are typically manifested through physiological changes, such as sweating, body temperature, and heart rate variations. This study utilized these three parameters to map participants’ emotional states within the emotional space proposed by Kołakowska (2015). To elicit controlled emotional responses (happiness, anger, sadness, and fear) virtual environ- ments were created using stimuli from the International Affective Picture System (IAPS) and the International Affective Digitized Sounds (IADS). Following data acquisition, signal processing techniques were applied to project the responses onto the emotional space, thereby enhancing the accuracy of emotional state monitoring. A neural network was subsequently trained for multiclass classification, achieving a 10% improvement in accuracy compared to previous models.
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