Yu Liangyue, Ge Yao, Ansari Shuja, Imran Muhammad, Ahmad Wasim
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Sensors (Basel). 2025 Aug 1;25(15):4763. doi: 10.3390/s25154763.
Emotion regulation is essential for mental health. However, many people ignore their own emotional regulation or are deterred by the high cost of psychological counseling, which poses significant challenges to making effective support widely available. This review systematically examines the convergence of multimodal sensing technologies and large language models (LLMs) for the development of Automated Emotional Regulation (AER) systems. The review draws upon a comprehensive analysis of the existing literature, encompassing research papers, technical reports, and relevant theoretical frameworks. Key findings indicate that multimodal sensing offers the potential for rich, contextualized data pertaining to emotional states, while LLMs provide improved capabilities for interpreting these inputs and generating nuanced, empathetic, and actionable regulatory responses. The integration of these technologies, including physiological sensors, behavioral tracking, and advanced LLM architectures, presents the improvement of application, moving AER beyond simpler, rule-based systems towards more adaptive, context-aware, and human-like interventions. Opportunities for personalized interventions, real-time support, and novel applications in mental healthcare and other domains are considerable. However, these prospects are counterbalanced by significant challenges and limitations. In summary, this review synthesizes current technological advancements, identifies substantial opportunities for innovation and application, and critically analyzes the multifaceted technical, ethical, and practical challenges inherent in this domain. It also concludes that while the integration of multimodal sensing and LLMs holds significant potential for AER, the field is nascent and requires concerted research efforts to realize its full capacity to enhance human well-being.
情绪调节对心理健康至关重要。然而，许多人忽视自身的情绪调节，或因心理咨询成本高昂而望而却步，这给广泛提供有效的支持带来了重大挑战。本综述系统地研究了多模态传感技术与大语言模型（LLMs）在自动化情绪调节（AER）系统开发中的融合情况。该综述基于对现有文献的全面分析，包括研究论文、技术报告及相关理论框架。主要研究结果表明，多模态传感有潜力提供与情绪状态相关的丰富、情境化数据，而大语言模型则具备更强的能力来解读这些输入，并生成细致入微、富有同理心且可付诸行动的调节反应。这些技术的整合，包括生理传感器、行为追踪和先进的大语言模型架构，推动了应用的改进，使AER从更简单的基于规则的系统迈向更具适应性、情境感知和类人化的干预措施。在心理保健和其他领域进行个性化干预、实时支持及开展新应用的机会相当可观。然而，这些前景也受到重大挑战和限制的制衡。总之，本综述综合了当前的技术进步，确定了大量创新和应用机会，并批判性地分析了该领域内在的多方面技术、伦理和实际挑战。它还得出结论，虽然多模态传感与大语言模型的整合对AER具有巨大潜力，但该领域尚处于起步阶段，需要共同的研究努力来充分发挥其增强人类福祉的能力。
