TRACE: Temporal Reasoning over Context and Evidence for Activity Recognition in Smart Homes
2026-05-04 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors explain that recognizing daily activities in smart homes is hard because many actions look similar to sensors and data is often incomplete. They created TRACE, a system that uses not just immediate sensor signals but also broader context and personal habits to better understand what people are doing. Their tests show TRACE is better at identifying complex activities, keeps predictions consistent over time, and works well even when some sensors are missing or used in new homes. This shows that considering wider context helps smart homes understand activities more accurately.
Human Activity RecognitionSmart HomesContextual ReasoningSensor DataTemporal CoherenceMulti-source EvidenceUser-specific PriorsCross-domain TransferMissing-Modality
Authors
Yingtian Shi, Abivishaq Balasubramanian, Jessica Herring, Jiachen Li, Juan Macias Romero, Rosemarie Santa Gonzalez, Varun Mishra, Agata Rozga, Xiang Zhi Tan, Thomas Plötz
Abstract
Human activity recognition (HAR) in smart homes remains challenging because many daily activities exhibit similar local sensor patterns, while minimally intrusive sensing provides sparse and ambiguous observations. As a result, methods based on short temporal or event windows often fail to capture the broader temporal and behavioral context needed for reliable activity understanding. We present TRACE (Temporal Reasoning over Context and Evidence), a contextual activity recognition framework for smart homes that integrates multi-source sensor evidence with user-specific contextual priors to improve activity interpretation. Rather than treating recognition as a local classification problem, TRACE leverages contextual reasoning to resolve ambiguities, reduce fragmented predictions, and infer more semantically specific activities. We evaluate TRACE on public benchmarks and in a deployment study conducted in our smart-home environment. Results show that TRACE improves recognition accuracy for semantically complex activities, produces more temporally coherent predictions that better align with user-specific routines, and maintains robust performance under cross-domain transfer and missing-modality conditions. These findings demonstrate the value of contextual reasoning for advancing smart-home HAR.