ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity
  Recognition Models
Luca Arrotta Claudio Bettini Gabriele Civitarese Michele Fiori
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.