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.

翻译：通过物联网传感设备收集数据并处理，实现智能家居中日常活动（ADL）的无干扰传感器识别，可支持医疗保健、安全及能源管理等应用。基于大语言模型（LLM）的零样本方法具有无需依赖标注ADL传感器数据的优势。然而，现有方法依赖于基于时间窗的分割策略，这与LLM的上下文推理能力匹配不佳。此外，现有方法缺乏预测置信度的估计方法。本文提出通过事件分割与一种新颖的预测置信度估计方法来改进零样本ADL识别。实验评估表明，在复杂真实数据集上，基于事件的分割方法持续优于基于时间的LLM方法，并超越了监督数据驱动方法，即使使用相对较小的LLM（如Gemma 3 27B）亦然。所提出的置信度度量能有效区分正确与错误预测。