MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectories

TL;DR

MARAuder's Map introduces a real-time human activity recognition framework that leverages spatial trajectories projected onto floorplans and advanced deep learning techniques to improve accuracy and robustness in smart home environments.

Contribution

The paper presents a novel approach combining trajectory-based spatial representations with deep learning and attention mechanisms for real-time activity recognition from unsegmented sensor data.

Findings

Outperforms existing HAR methods on real-world datasets

Effective in recognizing activities during transitions and ambiguous periods

Provides a practical solution for continuous, real-time monitoring

Abstract

Ambient sensor-based human activity recognition (HAR) in smart homes remains challenging due to the need for real-time inference, spatially grounded reasoning, and context-aware temporal modeling. Existing approaches often rely on pre-segmented, within-activity data and overlook the physical layout of the environment, limiting their robustness in continuous, real-world deployments. In this paper, we propose MARAuder's Map, a novel framework for real-time activity recognition from raw, unsegmented sensor streams. Our method projects sensor activations onto the physical floorplan to generate trajectory-aware, image-like sequences that capture the spatial flow of human movement. These representations are processed by a hybrid deep learning model that jointly captures spatial structure and temporal dependencies. To enhance temporal awareness, we introduce a learnable time embedding module that encodes contextual cues such as hour-of-day and day-of-week. Additionally, an attention-based encoder selectively focuses on informative segments within each observation window, enabling accurate recognition even under cross-activity transitions and temporal ambiguity. Extensive experiments on multiple real-world smart home datasets demonstrate that our method outperforms strong baselines, offering a practical solution for real-time HAR in ambient sensor environments.