用可穿戴数据+检索增强，为个人提供精准健康建议的智能助手
SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching
- 基于用户可穿戴设备数据构建个性化预测模型
- 检索专家内容提升建议可靠性，实验显示效果显著优于基线
- 适合关注健康科技与智能医疗的开发者和研究者
本文提出SePA（搜索增强型预测智能体），一种结合个性化机器学习与检索增强生成的新型大模型健康辅导系统，实现自适应、循证指导。SePA包含：(1) 基于28名用户、1260个数据点的可穿戴传感器数据，预测每日压力、酸痛与受伤风险的个性化模型；(2) 通过检索专家审核的网络内容，确保大模型生成建议的上下文相关性与可靠性。在滚动起源交叉验证和组k折交叉验证下，个性化模型表现优于通用基线。在小规模专家研究（n=4）中，基于检索的建议优于非检索基线，实际效应量（Cliff's δ=0.3，p=0.05）具意义。同时量化了响应质量与速度之间的延迟权衡，为下一代可信个人健康信息系统的构建提供透明蓝图。
原文摘要 · Abstract (English)
This paper introduces SePA (Search-enhanced Predictive AI Agent), a novel LLM health coaching system that integrates personalized machine learning and retrieval-augmented generation to deliver adaptive, evidence-based guidance. SePA combines: (1) Individualized models predicting daily stress, soreness, and injury risk from wearable sensor data (28 users, 1260 data points); and (2) A retrieval module that grounds LLM-generated feedback in expert-vetted web content to ensure contextual relevance and reliability. Our predictive models, evaluated with rolling-origin cross-validation and group k-fold cross-validation show that personalized models outperform generalized baselines. In a pilot expert study (n=4), SePA's retrieval-based advice was preferred over a non-retrieval baseline, yielding meaningful practical effect (Cliff's $δ$=0.3, p=0.05). We also quantify latency performance trade-offs between response quality and speed, offering a transparent blueprint for next-generation, trustworthy personal health informatics systems.
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