用事件触发分割提升零样本日常生活活动识别准确率
Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence
- 改用事件而非时间片段划分数据，更契合大模型上下文理解能力
- 在复杂真实数据集上表现优于传统方法，小模型也能达监督学习水平
- 提出新置信度评估方法，能有效区分正确与错误预测
智能家庭中通过物联网传感器无感识别日常生活活动（ADL）可支持医疗、安全与能源管理。基于大语言模型（LLM）的零样本方法无需标注数据，但现有方法依赖时间分段，与LLM的上下文推理能力不匹配，且缺乏预测置信度估计。本文提出基于事件的分段策略与新的置信度估算方法。实验表明，事件驱动分割在复杂真实数据集上持续优于时间分段方法，甚至超越部分有监督数据驱动方法，即便使用较小的LLM（如Gemma 3 27B）。所提置信度度量能有效区分正确与错误预测。
原文摘要 · Abstract (English)
Unobtrusive sensor-based recognition of Activities of Daily Living (ADLs) in smart homes by processing data collected from IoT sensing devices supports applications such as healthcare, safety, and energy management. Recent zero-shot methods based on Large Language Models (LLMs) have the advantage of removing the reliance on labeled ADL sensor data. However, existing approaches rely on time-based segmentation, which is poorly aligned with the contextual reasoning capabilities of LLMs. Moreover, existing approaches lack methods for estimating prediction confidence. This paper proposes to improve zero-shot ADL recognition with event-based segmentation and a novel method for estimating prediction confidence. Our experimental evaluation shows that event-based segmentation consistently outperforms time-based LLM approaches on complex, realistic datasets and surpasses supervised data-driven methods, even with relatively small LLMs (e.g., Gemma 3 27B). The proposed confidence measure effectively distinguishes correct from incorrect predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。