用大模型+检索实现零训练人体活动识别，支持未见动作识别。
RAG-HAR: Retrieval Augmented Generation-based Human Activity Recognition
- 基于大模型与向量库检索，不需训练即可识别动作。
- 在6个数据集上达到当前最佳性能，且可识别未见过的动作。
- 适合医疗、健康追踪等需要快速部署的场景。
人体活动识别（HAR）支撑医疗、康复、健身追踪和智能环境应用，但现有深度学习方法需特定数据集训练、大量标注数据及高计算资源。我们提出RAG-HAR，一种无需训练的检索增强生成框架，利用大语言模型（LLMs）进行HAR。RAG-HAR计算轻量级统计描述符，从向量数据库中检索语义相似样本，并利用上下文证据进行活动识别。进一步通过提示优化和引入基于大模型的活动描述符，构建上下文丰富的向量数据库，提升信息相关性。该框架在六个不同HAR基准上实现领先性能，且无需模型训练或微调，展现强鲁棒性与实用性。RAG-HAR突破已有行为限制，支持多个未见活动的识别与有意义标注。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) underpins applications in healthcare, rehabilitation, fitness tracking, and smart environments, yet existing deep learning approaches demand dataset-specific training, large labeled corpora, and significant computational resources.We introduce RAG-HAR, a training-free retrieval-augmented framework that leverages large language models (LLMs) for HAR. RAG-HAR computes lightweight statistical descriptors, retrieves semantically similar samples from a vector database, and uses this contextual evidence to make LLM-based activity identification. We further enhance RAG-HAR by first applying prompt optimization and introducing an LLM-based activity descriptor that generates context-enriched vector databases for delivering accurate and highly relevant contextual information. Along with these mechanisms, RAG-HAR achieves state-of-the-art performance across six diverse HAR benchmarks. Most importantly, RAG-HAR attains these improvements without requiring model training or fine-tuning, emphasizing its robustness and practical applicability. RAG-HAR moves beyond known behaviors, enabling the recognition and meaningful labelling of multiple unseen human activities.
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