SePA Web-Search Pipeline

Official implementation of the web-search pipeline from "SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching" (IEEE BHI 2025).

Code Release Status

Full code will be released by September 26, 2025

We are currently preparing the code for public release, including:

• Cleaning and documentation
• Removing any sensitive data/credentials
• Adding usage examples
• Testing installation procedures

Abstract

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 $\delta = 0.3$, 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.