Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
<|reference_start|>Large Language Models are Zero-Shot Recognizers for Activities of Daily Living: 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.<|reference_end|>
@article{civitarese2024large,
    title={Large Language Models are Zero-Shot Recognizers for Activities of Daily
  Living},
    author={Gabriele Civitarese, Michele Fiori, Priyankar Choudhary, Claudio
  Bettini},
    journal={arXiv preprint arXiv:2407.01238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01238},
    primaryClass={cs.AI cs.CL eess.SP}
}