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An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

Gouse Baig Mohammad1, Sirisha Potluri2, Ashwani Kumar3

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This summary is machine-generated.

This study introduces an AI system to detect patient emotions from remote healthcare data. The technology effectively classifies emotions, enhancing connected healthcare systems and patient-user bonds.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning Applications
  • Wearable Technology

Background:

  • Remote monitoring technologies are crucial for modern healthcare delivery.
  • Public perception of connected healthcare is influenced by factors like patient-user bonds.
  • Understanding patient emotions in remote healthcare is vital for practitioners.

Purpose of the Study:

  • To develop an AI-based classification system for detecting emotions from remote healthcare data.
  • To improve the integration and acceptance of wearable technology in healthcare.
  • To investigate the influence of patient emotional responses on connected healthcare systems.

Main Methods:

  • Utilized artificial intelligence (AI) for emotion detection.
  • Employed metaheuristic feature selection techniques.
  • Implemented machine learning classification algorithms.
  • The model involved preprocessing, feature selection, and classification stages.

Main Results:

  • The proposed AI model demonstrated effectiveness in classifying emotions from input datasets.
  • The system outperformed existing methods in emotion classification accuracy.
  • Simulations confirmed the model's efficacy across various dataset features.

Conclusions:

  • The developed AI classification system is effective for emotion detection in remote healthcare.
  • This technology can enhance patient-user engagement with connected healthcare systems.
  • The findings support the advancement of AI in improving patient experiences with remote monitoring.