Multimodal Sensing-Enabled Large Language Models for Automated Emotional Regulation: A Review of Current...

Area of Science:

• Digital Health
• Artificial Intelligence
• Mental Healthcare Technology

Background:

• Emotion regulation is crucial for mental health, yet access to support is limited by cost and awareness.
• Existing support systems often lack personalization and real-time adaptability.

Purpose of the Study:

• To systematically review the convergence of multimodal sensing and large language models (LLMs) for Automated Emotional Regulation (AER) systems.
• To explore the potential and challenges of integrating these technologies for enhanced mental well-being.

Main Methods:

• Comprehensive literature review of research papers, technical reports, and theoretical frameworks.
• Analysis of multimodal sensing capabilities (physiological, behavioral) and LLM advancements.

Main Results:

• Multimodal sensing provides rich emotional state data.
• LLMs enhance interpretation of emotional inputs and generation of tailored regulatory responses.
• Integration enables adaptive, context-aware AER systems beyond rule-based approaches.

Conclusions:

• The convergence of multimodal sensing and LLMs presents significant opportunities for personalized, real-time mental healthcare interventions.
• Realizing the full potential of AER requires addressing substantial technical, ethical, and practical challenges.
• The field is nascent, demanding further research to enhance human well-being through advanced AER.