Context-aware Human Activity Recognition (HAR) is a hot research area in
mobile computing, and the most effective solutions in the literature are based
on supervised deep learning models. However, the actual deployment of these
systems is limited by the scarcity of labeled data that is required for
training. Neuro-Symbolic AI (NeSy) provides an interesting research direction
to mitigate this issue, by infusing common-sense knowledge about human
activities and the contexts in which they can be performed into HAR deep
learning classifiers. Existing NeSy methods for context-aware HAR rely on
knowledge encoded in logic-based models (e.g., ontologies) whose design,
implementation, and maintenance to capture new activities and contexts require
significant human engineering efforts, technical knowledge, and domain
expertise. Recent works show that pre-trained Large Language Models (LLMs)
effectively encode common-sense knowledge about human activities. In this work,
we propose ContextGPT: a novel prompt engineering approach to retrieve from
LLMs common-sense knowledge about the relationship between human activities and
the context in which they are performed. Unlike ontologies, ContextGPT requires
limited human effort and expertise. An extensive evaluation carried out on two
public datasets shows how a NeSy model obtained by infusing common-sense
knowledge from ContextGPT is effective in data scarcity scenarios, leading to
similar (and sometimes better) recognition rates than logic-based approaches
with a fraction of the effort.

基于深度学习模型，上下文感知的人类活动识别是移动计算领域的热点研究方向。然而，这些系统的实际部署受到标注数据稀缺的限制。基于神经符号人工智能领域的研究提出了使用常识知识来缓解此问题，在人类活动识别深度学习分类器中融入关于人类活动及其上下文的常识知识。本研究中，我们提出了 ContextGPT: 一种新的提示工程方法，用于从大型语言模型中检索关于人类活动与上下文关系的常识知识。与本体论不同，ContextGPT 所需的人力和专业知识较少。在两个公共数据集上进行的广泛评估表明，通过从 ContextGPT 中融入常识知识获得的 NeSy 模型，在数据稀缺场景中具有类似甚至更好的识别率，比基于逻辑的方法需要更少的工作量。