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

• URL: http://arxiv.org/abs/2511.05773v1
• Date: Sat, 08 Nov 2025 00:07:43 GMT
• Title: MARAuder's Map: Motion-Aware Real-time Activity Recognition with Layout-Based Trajectories
• Authors: Zishuai Liu, Weihang You, Jin Lu, Fei Dou,
• Abstract summary: We propose a novel framework for real-time activity recognition from raw, unsegmented sensor streams.<n>Our method projects sensor activations onto the physical floorplan to generate trajectory-aware, image-like sequences.<n>To enhance temporal awareness, we introduce a learnable time embedding module that encodes contextual cues.
• Score: 3.788163163289351
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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