  The sensor-based recognition of Activities of Daily Living (ADLs) in smart
home environments enables several applications in the areas of energy
management, safety, well-being, and healthcare. ADLs recognition is typically
based on deep learning methods requiring large datasets to be trained.
Recently, several studies proved that Large Language Models (LLMs) effectively
capture common-sense knowledge about human activities. However, the
effectiveness of LLMs for ADLs recognition in smart home environments still
deserves to be investigated. In this work, we propose ADL-LLM, a novel
LLM-based ADLs recognition system. ADLLLM transforms raw sensor data into
textual representations, that are processed by an LLM to perform zero-shot ADLs
recognition. Moreover, in the scenario where a small labeled dataset is
available, ADL-LLM can also be empowered with few-shot prompting. We evaluated
ADL-LLM on two public datasets, showing its effectiveness in this domain.
