Wearable sensor devices, which offer the advantage of recording daily objects used by a person while performing an activity, enable the feasibility of unsupervised Human Activity Recognition (HAR). Unfortunately, previous unsupervised approaches using the usage sequence of objects usually require a proper description of activities manually prepared by humans. Instead, we leverage the knowledge embedded in a Large Language Model (LLM) of ChatGPT. Because the sequence of objects robustly characterizes the activity identity, it is possible that ChatGPT already learned the association between activities and objects from existing contexts. However, previous prompt engineering for ChatGPT exhibits limited generalization ability when dealing with a list of words (i.e., sequence of objects) due to the similar weighting assigned to each word in the list. In this study, we propose a two-stage prompt engineering, which first guides ChatGPT to generate activity descriptions associated with objects while emphasizing important objects for distinguishing similar activities; then outputs activity classes and explanations for enhancing the contexts that are helpful for HAR. To the best of our knowledge, this is the first study that utilizes ChatGPT to recognize activities using objects in an unsupervised manner. We conducted our approach on three datasets and demonstrated the state-of-the-art performance.

翻译：可穿戴传感器设备能够记录人在执行活动时使用的日常物体，这使得无监督人类活动识别成为可能。遗憾的是，以往利用物体使用序列的无监督方法通常需要人工手动准备活动的恰当描述。相反，我们利用了ChatGPT大型语言模型中蕴含的知识。由于物体序列能稳健地表征活动身份，ChatGPT可能已从现有语境中习得了活动与物体之间的关联。然而，先前针对ChatGPT的提示工程在处理单词列表（即物体序列）时，因列表中每个单词被赋予相似权重而表现出有限的泛化能力。本研究提出了一种两阶段提示工程方法：首先引导ChatGPT生成与物体相关的活动描述，同时突出有助于区分相似活动的重要物体；随后输出活动类别及解释，以增强对HAR有益的上下文。据我们所知，这是首次利用ChatGPT以无监督方式通过物体进行活动识别的研究。我们在三个数据集上验证了该方法，并展示了最先进的性能。