Emerging trends and clinical challenges in AI-enhanced emotion diagnosis using physiological data

Area of Science:

• Physiological Psychology
• Computational Neuroscience
• Machine Learning

Background:

• Emotional states significantly impact physiological parameters such as heart rate, respiration, and electroencephalography (EEG).
• Understanding these physiological changes is crucial for advancing emotion research and recognition.

Purpose of the Study:

• To explore the relationship between physiological parameters and emotions.
• To evaluate the potential of machine learning (ML) for emotion recognition.
• To discuss applications and challenges in emotion recognition technology.

Main Methods:

• Review of existing literature on physiological correlates of emotions.
• Introduction of machine learning and deep learning models for emotion recognition.
• Focus on heart rate variability (HRV) as a key physiological indicator.

Main Results:

• Physiological parameters like HRV are reliable indicators of emotional states.
• ML models can effectively detect emotions from physiological data.
• Emotion recognition technologies show promise in clinical psychology and health monitoring.

Conclusions:

• Machine learning-based emotion recognition offers significant potential for mental health assessment, diagnosis, and treatment guidance.
• HRV is a key parameter for precise emotional analysis, enhancing emotion management and health monitoring tools.
• Challenges related to data collection, privacy, and individual variability must be addressed for widespread adoption.