根据用户细微情绪生成定制化疗愈音乐，提升心理舒适度。
EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emotions
- 用微表情识别技术分析文本情绪，匹配音乐参数
- 用户情绪改善显著，感知准确率超4分（满分5分）
- 适合心理健康应用、数字疗愈产品开发者参考
现有数字心理健康工具常忽视日常挑战中的细微情绪。例如，全球有超过15亿人受睡前焦虑困扰，但现有方法多为静态通用模式，无法个性化适配。本文提出EmoHeal系统，通过三阶段支持叙事实现个性化疗愈：利用微调的XLM-RoBERTa模型从用户文本中检测27种细粒度情绪，并基于音乐治疗原则知识图谱（GEMS，同质性原理）映射至音乐参数；再通过CLAMP3模型检索音视频内容，引导用户从当前状态向更平静状态过渡（匹配-引导-目标）。一项针对40名用户的自身对照研究显示，参与者情绪显著改善（均值=4.12，p<0.001），且对情绪识别感知准确度高（均值=4.05，p<0.001）。感知准确度与治疗效果呈强相关（r=0.72，p<0.001），验证了细粒度情绪驱动的有效性。研究证明理论驱动的情绪感知数字健康工具可行，并为可扩展的音乐治疗人工智能实现提供蓝图。
原文摘要 · Abstract (English)
Existing digital mental wellness tools often overlook the nuanced emotional states underlying everyday challenges. For example, pre-sleep anxiety affects more than 1.5 billion people worldwide, yet current approaches remain largely static and "one-size-fits-all", failing to adapt to individual needs. In this work, we present EmoHeal, an end-to-end system that delivers personalized, three-stage supportive narratives. EmoHeal detects 27 fine-grained emotions from user text with a fine-tuned XLM-RoBERTa model, mapping them to musical parameters via a knowledge graph grounded in music therapy principles (GEMS, iso-principle). EmoHeal retrieves audiovisual content using the CLAMP3 model to guide users from their current state toward a calmer one ("match-guide-target"). A within-subjects study (N=40) demonstrated significant supportive effects, with participants reporting substantial mood improvement (M=4.12, p<0.001) and high perceived emotion recognition accuracy (M=4.05, p<0.001). A strong correlation between perceived accuracy and therapeutic outcome (r=0.72, p<0.001) validates our fine-grained approach. These findings establish the viability of theory-driven, emotion-aware digital wellness tools and provides a scalable AI blueprint for operationalizing music therapy principles.
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