不用提示词，用语言模型直接识别家居行为
Thou Shalt Not Prompt: Zero-Shot Human Activity Recognition in Smart Homes via Language Modeling of Sensor Data & Activities
- 将传感器数据与行为转为自然语言，用嵌入向量实现零样本分类
- 在6个数据集上验证，无需提示词也能达到良好识别效果
- 解决隐私泄露和外部依赖问题，适合跨场景智能家居应用
开发零样本人类活动识别（HAR）方法是智能家居研究的关键方向，尤其在面对不同传感模态、布局和关注活动的场景时。当前主流方法通过生成传感器数据的自然语言描述，并经精心设计的提示输入大语言模型（LLM）进行分类。尽管性能良好，但这类“提示LLM”方法存在隐私泄露、依赖外部服务及版本变更导致预测不一致等风险，因此亟需无需提示的替代方案。本文提出一种新方法：将传感器数据与活动建模为自然语言，利用其嵌入向量实现零样本分类，从而避免向LLM提问。本工作的价值在于在六个数据集上开展详尽案例研究，证明语言建模可显著提升零样本HAR系统的性能。
原文摘要 · Abstract (English)
Developing zero-shot human activity recognition (HAR) methods is a critical direction in smart home research -- considering its impact on making HAR systems work across smart homes having diverse sensing modalities, layouts, and activities of interest. The state-of-the-art solutions along this direction are based on generating natural language descriptions of the sensor data and feeding it via a carefully crafted prompt to the LLM to perform classification. Despite their performance guarantees, such ``prompt-the-LLM'' approaches carry several risks, including privacy invasion, reliance on an external service, and inconsistent predictions due to version changes, making a case for alternative zero-shot HAR methods that do not require prompting the LLMs. In this paper, we propose one such solution that models sensor data and activities using natural language, leveraging its embeddings to perform zero-shot classification and thereby bypassing the need to prompt the LLMs for activity predictions. The impact of our work lies in presenting a detailed case study on six datasets, highlighting how language modeling can bolster HAR systems in zero-shot recognition.
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