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Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

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GIDINT, Faculty of Systems Engineering, Universidad Santo Tomás Seccional Tunja, Calle 19, No. 11-64, Tunja 150001, Colombia
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SysTémico Research Group, Department of Tourism Sciences, Universidad del Cauca, Calle 5, No. 4-70, Popayán 190002, Colombia
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GAST, Telematics Engineering Department, Universidad Carlos III de Madrid, Av. de la Universidad, 30, 28911 Madrid, Spain
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GIT, Telematics Department, Universidad del Cauca, Calle 5, No. 4-70, Popayán 190002, Colombia
*
Author to whom correspondence should be addressed.
Academic Editor: Giovanni Andrea Casula
Sensors 2021, 21(23), 7854; https://doi.org/10.3390/s21237854
Received: 1 October 2021 / Revised: 18 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Use of Smart Wearable Sensors and AI Methods in Providing P4 Medicine)
The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance. View Full-Text
Keywords: CNN; emotion detection; IoT; heart rate; LSTM; recommender system; tourist experience; wearable; xiaomi mi band CNN; emotion detection; IoT; heart rate; LSTM; recommender system; tourist experience; wearable; xiaomi mi band
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MDPI and ACS Style

Santamaria-Granados, L.; Mendoza-Moreno, J.F.; Chantre-Astaiza, A.; Munoz-Organero, M.; Ramirez-Gonzalez, G. Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data. Sensors 2021, 21, 7854. https://doi.org/10.3390/s21237854

AMA Style

Santamaria-Granados L, Mendoza-Moreno JF, Chantre-Astaiza A, Munoz-Organero M, Ramirez-Gonzalez G. Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data. Sensors. 2021; 21(23):7854. https://doi.org/10.3390/s21237854

Chicago/Turabian Style

Santamaria-Granados, Luz, Juan F. Mendoza-Moreno, Angela Chantre-Astaiza, Mario Munoz-Organero, and Gustavo Ramirez-Gonzalez. 2021. "Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data" Sensors 21, no. 23: 7854. https://doi.org/10.3390/s21237854

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