EmoWeb: An Infrastructure for Adapting User Interfaces Based on Desired Emotions

Alex Sandro Rodrigues Ancioto; Rogério Aparecido Campanari Xavier; Fernando Miguelão da Silva; Patrícia Deud Guimarães; Letícia Gabrielly Zacano da Silva; Valter Vieira de Camargo; Vania Paula de Almeida Neris

doi:10.5753/jis.2026.6834

Authors

Alex Sandro Rodrigues Ancioto Federal Institute of Education, Science and Technology of São Paulo https://orcid.org/0000-0003-1803-4279
Rogério Aparecido Campanari Xavier Federal Institute of Education, Science and Technology of São Paulo https://orcid.org/0000-0001-8553-5706
Fernando Miguelão da Silva Federal University of Sao Carlos https://orcid.org/0009-0003-4441-3579
Patrícia Deud Guimarães Federal University of Sao Carlos https://orcid.org/0009-0006-4027-0314
Letícia Gabrielly Zacano da Silva Federal University of Sao Carlos https://orcid.org/0000-0003-4309-3238
Valter Vieira de Camargo Federal University of Sao Carlos https://orcid.org/0000-0002-6439-4649
Vania Paula de Almeida Neris Federal University of Sao Carlos https://orcid.org/0000-0002-4059-8700

DOI:

https://doi.org/10.5753/jis.2026.6834

Keywords:

User Interface, Emotions, Sensors, MAPE-K, Infrastructure

Abstract

This article systematizes and analyzes the evolutionary development of EmoWeb, a computational infrastructure designed to adapt web interfaces according to users’ desired emotional states. Although the integration of emotional models into adaptive systems has received increasing attention in Human–Computer Interaction (HCI), consolidated studies examining the architectural evolution and longitudinal validation of such infrastructures remain scarce. Grounded in Scherer’s Semantic Space of Emotions and structured according to the IBM MAPE-K self-adaptive architecture, the proposed infrastructure integrates theoretical, methodological, and technical contributions derived from a longitudinal research program. This trajectory encompasses hybrid approaches to emotional assessment; the design of an adaptive architecture; studies on visual interface elements; emotion recognition using physiological sensors and machine learning techniques; and the development of self-report instruments. The infrastructure was evaluated through a comparative architectural analysis focusing on software maintainability, as well as a controlled user experiment that assessed its potential to guide users toward desired emotional states. The results indicate improvements in maintainability and architectural flexibility, along with a tendency for users to shift toward more positive emotional states. These findings highlight the potential of EmoWeb as a foundation for emotion-oriented adaptive interfaces and contribute to advances in HCI, particularly regarding the personalization of users’ emotional experiences, in alignment with the GranDIHC-BR 2025–2035 agenda.

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