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

TL;DR

TRACE is a framework that enhances activity recognition in smart homes by integrating multi-source sensor data with user-specific context, leading to more accurate and coherent activity predictions.

Contribution

It introduces a novel contextual reasoning approach that combines sensor evidence and user priors, outperforming traditional local classification methods in activity recognition.

Findings

Improves recognition accuracy for complex activities

Produces more temporally coherent predictions

Maintains performance under cross-domain transfer and missing data

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.