Large Language Models are Zero-Shot Recognizers for Activities of Daily Living

• URL: http://arxiv.org/abs/2407.01238v2
• Date: Tue, 08 Oct 2024 13:31:09 GMT
• Title: Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
• Authors: Gabriele Civitarese, Michele Fiori, Priyankar Choudhary, Claudio Bettini,
• Abstract summary: We propose ADL-LLM, a novel LLM-based ADLs recognition system. ADL-LLM transforms raw sensor data into textual representations, that are processed by an LLM to perform zero-shot ADLs recognition. We evaluate ADL-LLM on two public datasets, showing its effectiveness in this domain.
• Score: 0.29998889086656577
• License:
• Abstract: 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.

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