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

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.