RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
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
Record details
Published: 29 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Privacy · Healthcare · Environment
Retrieved: 30 July 2026
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ethics.ai (29 July 2026), “RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment,” evidence record 14853, https://ethics.ai/record/14853 (originally published by arXiv cs.LG).
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