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Domestic dogs exhibit a wide variety of behaviors that convey their physical needs, emotional states, and interaction preferences. Accurate, real‑time recognition of these behaviors can enable smarter home‑automation, improve animal welfare, and assist owners with training or health monitoring. This paper presents a framework that continuously ingests multimodal sensor streams (RGB‑D video, audio, inertial measurement units) from a low‑cost home‑installed sensor suite and produces on‑device, sub‑second predictions of a predefined set of dog behaviors (e.g., sitting, barking, pacing, chewing, distress). We introduce a novel Temporal‑Fusion Convolutional‑Recurrent Network (TF‑CRN) that combines spatial feature extraction, temporal attention, and sensor‑fusion layers. The system is evaluated on a newly collected dataset of 1 200 hours of annotated dog activity from 30 households, achieving 92.4 % weighted F1‑score while maintaining an average latency of 180 ms on a Raspberry‑Pi‑4 edge device. We also discuss privacy‑preserving design choices, energy efficiency, and potential extensions to other companion animals. As the days went by, Tante Momo became
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