Sleep is vital for health, yet access to data alone does not guarantee improvement. While wearables and health apps enable tracking, users face a "Data-Action Gap," struggling to interpret metrics and translate them into action. Current interventions fail to bridge this: static dashboards lack context, rule-based agents rely on rigid scripts, and LLM-agents lack grounding in personal data, causing trust issues. We propose SAGE (Sensor-Augmented Grounding Engine) for an LLM-powered sleep care agent. SAGE normalizes continuous sleep, physiological, and activity data from the sensors into a queryable time-series layer. It supports (1) selective system-initiated monitoring that triggers notifications only upon detecting meaningful deviations against personal baselines to reduce alert fatigue, and (2) user-initiated Q&A where natural language is translated into executable database queries. By ensuring responses are grounded in precise period, comparison, and metric data, SAGE aims to enhance personalization, traceability, and trust, articulating a novel design space for evidence-based messaging in sleep care.
@article{arxiv.2604.16342,
title = {SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent},
author = {Hansoo Lee and Yoonjae Cho and Sonya S. Kwak and Rafael A. Calvo},
journal= {arXiv preprint arXiv:2604.16342},
year = {2026}
}
Comments
Accepted to the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26). 6 pages