{
  "id": 14853,
  "url": "https://arxiv.org/abs/2607.26631v1",
  "title": "RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment",
  "summary": "Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning approaches require dataset-specific training, large labeled corpora, and repeated adaptation to new sensor settings or activity taxonomies. Retrieval-Augmented Generation for Human Activity Recognition (RAG-HAR) addresses this by framing HAR as a training-free, retrieval-augmented task, in which statistical descriptions",
  "authors": "Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha, Anura Jayasumana, Kanchana Thilakarathna",
  "category": "research",
  "topics": "privacy-surveillance,healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T08:57:03.000Z",
  "fetched_at": "2026-07-30T05:10:24.387Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14853",
  "original_url": "https://arxiv.org/abs/2607.26631v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}