Publicação
Emotional regulation assessment via multi-biosignal processing in a VR environment for neurorehabilitation
| Resumo: | Emerging immersive technologies and physiological computing capabilities are opening promising pathways for emotion recognition and regulation, with growing relevance in fields such as affective computing, neurorehabilitation, and human-computer interaction. Through four exploratory studies, this thesis investigates how virtual reality, biofeed back, and machine learning can be combined to recognize and regulate users’ emotional states in real time. First, a machine learning pipeline was developed to classify emotional states using physiological signals collected in immersive and non-immersive virtual reality conditions. Results showed that immersion had a limited impact on subjective emotional ratings, while user-dependent models significantly outperformed user-independent ones, highlighting the importance of personalization in emotion recognition. The second study validated this pipeline in individuals with Alzheimer’s, revealing that emotional reactivity is partially preserved across severity levels. Classification models successfully distinguished between emotional states, healthy and Alzheimer’s participants, and even Alzheimer’s severity levels, underscoring the pipeline’s clinical relevance and generalization. The third study introduced a nature-based virtual reality environment, the Virtual Lev ada, which used real-time adaptation to users’ physiological stress levels via a biofeedback mechanism. This study also implemented and evaluated real-time retraining strategies for the stress classification model, addressing temporal drift and improving model robust ness. Although biofeedback effects were not statistically significant, both adaptive and non-adaptive groups reported reduced physiological arousal and anxiety, supporting the environment’s calming and restorative potential. Finally, the fourth study improved the adaptive virtual reality system by integrating online stress predictions and online model retraining. Results demonstrated improved prediction stability over time and significant reductions in state anxiety, particularly in individuals with elevated stress levels. In conclusion, these findings validate the feasibility and effectiveness of progressively adaptive, personalized virtual reality systems for emotion recognition and regulation. This work contributes with novel insights into how online physiological monitoring and ML adaptation can enhance emotional self-regulation, offering promising directions for affective technologies development and mental health interventions. |
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| Autores principais: | Lima, Rodrigo Olival |
| Assunto: | Affective computing Adaptive Anxiety Biofeedback Depressive symptomatology Emotion regulation Human-computer interaction Machine learning Neurorehabilitation Physiological signals Stress Virtual reality Adaptação Ansiedade Computação afetiva Interação humano-computador Neurorehabilitação Realidade virtual Regulação emocional Sinais fisiológicos Sintomas depressivos Informatics Engineering specialization: Human-Computer Interaction . Faculdade de Ciências Exatas e da Engenharia |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade da Madeira |
| Idioma: | inglês |
| Origem: | DigitUMa - Repositório da Universidade da Madeira |
| Resumo: | Emerging immersive technologies and physiological computing capabilities are opening promising pathways for emotion recognition and regulation, with growing relevance in fields such as affective computing, neurorehabilitation, and human-computer interaction. Through four exploratory studies, this thesis investigates how virtual reality, biofeed back, and machine learning can be combined to recognize and regulate users’ emotional states in real time. First, a machine learning pipeline was developed to classify emotional states using physiological signals collected in immersive and non-immersive virtual reality conditions. Results showed that immersion had a limited impact on subjective emotional ratings, while user-dependent models significantly outperformed user-independent ones, highlighting the importance of personalization in emotion recognition. The second study validated this pipeline in individuals with Alzheimer’s, revealing that emotional reactivity is partially preserved across severity levels. Classification models successfully distinguished between emotional states, healthy and Alzheimer’s participants, and even Alzheimer’s severity levels, underscoring the pipeline’s clinical relevance and generalization. The third study introduced a nature-based virtual reality environment, the Virtual Lev ada, which used real-time adaptation to users’ physiological stress levels via a biofeedback mechanism. This study also implemented and evaluated real-time retraining strategies for the stress classification model, addressing temporal drift and improving model robust ness. Although biofeedback effects were not statistically significant, both adaptive and non-adaptive groups reported reduced physiological arousal and anxiety, supporting the environment’s calming and restorative potential. Finally, the fourth study improved the adaptive virtual reality system by integrating online stress predictions and online model retraining. Results demonstrated improved prediction stability over time and significant reductions in state anxiety, particularly in individuals with elevated stress levels. In conclusion, these findings validate the feasibility and effectiveness of progressively adaptive, personalized virtual reality systems for emotion recognition and regulation. This work contributes with novel insights into how online physiological monitoring and ML adaptation can enhance emotional self-regulation, offering promising directions for affective technologies development and mental health interventions. |
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