TRACE: Temporal Reasoning over Context and Evidence for Activity Recognition in Smart Homes

2026-05-04

AI Summary

This work addresses the challenges of human activity recognition in smart homes, where sparse sensor signals and similar local patterns hinder accurate modeling of semantically complex daily behaviors. To overcome these limitations, the authors propose TRACE, a framework that reframes activity recognition as a context-aware temporal reasoning task rather than isolated local classification. By integrating multi-source sensor evidence with user-specific contextual priors, TRACE enables coherent and robust semantic inference. The approach effectively mitigates prediction fragmentation and significantly improves recognition accuracy for complex activities on both public benchmarks and real-world deployments. Furthermore, it demonstrates consistent robustness under cross-domain scenarios and in the presence of missing modalities.

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.

Problem

• Human Activity Recognition
• Smart Homes
• Temporal Context
• Sensor Ambiguity
• Contextual Reasoning

Innovation

• contextual reasoning
• temporal modeling
• activity recognition
• smart homes
• multi-modal fusion