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A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases.

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A Systematic Review for Cognitive State-Based QoE/UX Evaluation.

Edgar Bañuelos-Lozoya1, Gabriel González-Serna1, Nimrod González-Franco1

  • 1Department of Computer Science, TecNM/CENIDET, Morelos 62490, Mexico.

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This review explores cognitive states in user experience (UX) evaluation, using sensor data to understand user interactions. It highlights methods for interpreting mental workload and stress for better UX design.

Keywords:
QoEUXbehavioral databiometric sensorscognitive statesphysiological data

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Data Science

Background:

  • Traditional user experience (UX) evaluation is subjective.
  • Objective assessment requires interpreting user cognitive states via physiological and behavioral data.
  • Understanding cognitive states enhances Quality of Experience (QoE) evaluation.

Purpose of the Study:

  • To systematically review cognitive states investigated in QoE/UX evaluation.
  • To identify signals, machine learning models, and architectures used.
  • To analyze achieved results and propose future directions.

Main Methods:

  • Systematic literature review of 29 papers (2014-2019) from eight sources.
  • Analysis of cognitive states, signals, machine learning models, and evaluation architectures.
  • Categorization of studies based on focus: classification, architectures, or correlations.

Main Results:

  • Identified cognitive states like mental workload, confusion, and stress.
  • Found 24% of studies focused on cognitive state classification.
  • 41% of studies correlated signals, cognitive states, and QoE/UX metrics.

Conclusions:

  • The number of studies is relatively low compared to other critical domains.
  • This review provides a foundation for analyzing cognitive states in UX.
  • Future work can develop more robust QoE/UX evaluation architectures using human signals.