In this section
@inproceedings{Lee:2026:10.1145/3772363.3798959,
author = {Lee, H and Cho, Y and Kwak, SS and Calvo, RA},
doi = {10.1145/3772363.3798959},
title = {SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent},
url = {http://dx.doi.org/10.1145/3772363.3798959},
year = {2026}
}
TY - CPAPER
AB - 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.
AU - Lee,H
AU - Cho,Y
AU - Kwak,SS
AU - Calvo,RA
DO - 10.1145/3772363.3798959
PY - 2026///
TI - SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent
UR - http://dx.doi.org/10.1145/3772363.3798959
ER -