A Neural Model with a Deep Learning Structure for Characterizing Relaxation Levels Through Olfactory Stimuli to Enhance the Guest Experience in Hotels

Nowadays, people have experienced increased stress levels, and sometimes they turn to travel and tourism to solve this. In this order, the hotel industry has sought to generate experiences and products that generate well-being and recall in their guests. Within these experiences, we find sensory experiences, where some studies have shown that, for example, a scented environment can cause happiness and relief in customers staying in hotel rooms. In this study, we propose a methodology based on a deep learning architecture for emotion recognition by subjecting a group of women to a relaxing olfactory stimulus after being subjected to a stress-producing stimulus to test whether the desired relaxing effect is indeed achieved. To this end, we use EEG recordings and subsequent analysis, relying on the union of neuroscience and artificial intelligence. The study here shows that the proposed deep learning architecture can identify the state of relaxation and stress and identifies which of the olfactory stimuli causes the most intense relaxation.