Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These shallower annotations ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, first analyze this lack of granular annotations from available pre-annotated datasets to understand the practical inconsistencies and also perform a detailed survey to look into the human perception surrounding annotations. Drawing motivations from these, we next develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures and the available coarse-grain macro-activity labels. In the backend, AmicroN applies change-point detection followed by zero-shot learning with activity embeddings to identify the unseen micro-activities in an unsupervised manner. Rigorous evaluation on publicly available datasets shows that AmicroN can accurately generate micro-activity annotations with a median F1-score of >0.75. Additionally, we also show that AmicroN can be used in a plug-and-play manner with Large Language Models (LLMs) to obtain the micro-activity labels, thus making it more practical for realistic applications.

翻译：利用传感器数据进行高效人类活动识别需要大量带标注数据。日益增长的无标签传感器数据挑战了传统采用人机交互方式收集人类活动识别标注的实践，导致常收集到浅层标注。这些浅层标注忽略了构成日常生活复杂活动的细粒度微活动。基于此，本文首先分析现有预标注数据集缺乏精细标注的问题，以理解实际中的不一致性，并通过详细调研探究人类对标注的认知。受此启发，我们开发了自动生成微活动标注的AmicroN框架，该框架利用运动特征与已有的粗粒度宏观活动标签。在后端，AmicroN采用变点检测结合基于活动嵌入的零样本学习，以无监督方式识别未见的微活动。在公开数据集上的严格评估表明，AmicroN能够准确生成微活动标注，中位F1分数超过0.75。此外，我们还展示AmicroN可以即插即用方式与大语言模型结合获取微活动标签，从而提升其在现实应用中的实用性。