Smaller AI, Smarter Homes: Distilling LLMs for Human Activity Recognition

Kari Jaaskelainen

13 Jan 2026 — 1 min read

How can smart homes understand daily activities without heavy, power-hungry AI? A new study tests large language models (LLMs) for Human Activity Recognition (HAR) in homes.

What they found

Model size matters: Recognition accuracy improves as LLMs get larger.
Knowledge distillation works: The team fine-tuned smaller LLMs using HAR reasoning examples generated by larger LLMs.
Big results, small models: Distilled small models performed almost as well as the largest ones while using about 50× fewer parameters.
Tested on strong benchmarks: Results were shown on two state-of-the-art HAR datasets.

Why it matters: Smaller, high-performing models could make context-aware and assisted-living applications more efficient and accessible.

Paper by Julien Cumin, Oussama Er-Rahmany, and Xi Chen.

Read more: https://arxiv.org/abs/2601.07469v1

Paper: https://arxiv.org/abs/2601.07469v1

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AI SmartHome HAR LLM KnowledgeDistillation EdgeAI AssistiveTech MachineLearning Research

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