SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908974886944768 |
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| author | Lee, Hansoo Cho, Yoonjae Kwak, Sonya S. Calvo, Rafael A. |
| author_facet | Lee, Hansoo Cho, Yoonjae Kwak, Sonya S. Calvo, Rafael A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16342 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent Lee, Hansoo Cho, Yoonjae Kwak, Sonya S. Calvo, Rafael A. Human-Computer Interaction H.5.2; J.3; I.2.7 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. |
| title | SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent |
| topic | Human-Computer Interaction H.5.2; J.3; I.2.7 |
| url | https://arxiv.org/abs/2604.16342 |