Remind Me To Check The Stove Before I Leave The House: Authoring Personalized Context-Aware Smart Home Reminders Using Everyday Language

2026-05-21

AI Summary

This work addresses the limitations of existing reminder systems in leveraging the multimodal sensing capabilities of smart homes and the lack of natural means for users to express complex, context-aware reminders. We propose a novel framework based on natural language and conversational interaction that enables users to flexibly specify multidimensional reminder intents—including temporal, activity-based, sensor-derived, and state-dependent conditions—using everyday language. Through guided dialogue, the system structures ambiguous user expressions into executable rules. The architecture integrates natural language understanding, context-aware computation, sensor fusion, and a rule-based reasoning engine. Two user studies (N=40 and N=10) demonstrate that our approach effectively handles diverse and complex reminder logic, significantly improving alignment between user intent and system interpretation.

Abstract

Reminder systems commonly rely on fixed schedules, location triggers, or simple rules, limiting their ability to leverage the rich sensing capabilities of modern smart homes. A key challenge lies in enabling users to specify context-aware reminders without requiring complex configurations. We present a system pipeline that supports reminder authoring through natural language and conversational interaction. The pipeline translates user requests into structured representations and executable logic, incorporating time-based, activity-based, sensor-based, and state-based conditions. We conducted two studies to examine how users express reminder intent and how conversational support influences the authoring process. In Study 1 (N=40), we analyzed 233 user-authored reminders and identified challenges in expressing reminders with diverse and complex logic. Based on these findings, we refined the system and evaluated it in Study 2 (N=10), demonstrating improved handling of time-based, activity-based, sensor-based, and state-based conditions. Our results highlight the diversity and ambiguity of user expressions and show that conversational guidance can help structure these expressions into flexible, context-aware reminders.

Problem

• context-aware reminders
• smart home
• natural language
• reminder authoring
• conversational interaction

Innovation

• natural language interface
• context-aware reminders
• smart home
• conversational interaction
• reminder authoring