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Labeling Emotion01:20

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data.

Luz Santamaria-Granados1, Juan Francisco Mendoza-Moreno1, Angela Chantre-Astaiza2

  • 1GIDINT, Faculty of Systems Engineering, Universidad Santo Tomás Seccional Tunja, Calle 19, No. 11-64, Tunja 150001, Colombia.

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This study introduces a tourist recommendation system using wearable device heart rate (HR) data for emotion recognition (ER). A hybrid deep learning model achieved promising results for ER and tourist recommendations.

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CNNIoTLSTMemotion detectionheart raterecommender systemtourist experiencewearablexiaomi mi band

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Affective Computing

Background:

  • Consumer-grade wearable devices enable widespread physiological data collection.
  • Low-accuracy sensors offer potential for novel applications beyond healthcare.
  • Real-world emotion recognition (ER) from physiological data remains challenging.

Purpose of the Study:

  • Propose a tourist experiences recommender system (TERS) architecture.
  • Develop an emotion recognition (ER) model using heart rate (HR) data from wearables in daily life.
  • Integrate ER into a TERS to personalize tourist recommendations.

Main Methods:

  • Collected HR measurements and user-labeled emotions via mobile applications.
  • Employed deep learning algorithms, including hybrid Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for ER.
  • Designed and validated the Tourist Experience Recommender System - Emotion Recognition (TERS-ER) using Collaborative Filtering (CF) with CNN.

Main Results:

  • Generated a dataset of HR measurements labeled with emotions from daily life.
  • The CNN-LSTM hybrid model demonstrated promising performance for ER from HR data.
  • Collaborative Filtering (CF) enhanced with CNN achieved superior performance for the TERS.

Conclusions:

  • Emotion recognition from wearable HR data in real-world settings is feasible.
  • Deep learning models, particularly CNN-LSTM, are effective for ER using HR data.
  • The proposed TERS-ER system effectively integrates emotion recognition for personalized tourist recommendations.