Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
This addresses ADLs recognition for smart home applications like healthcare and energy management, but it is incremental as it applies existing LLM capabilities to a new domain.
The paper tackles the problem of recognizing Activities of Daily Living (ADLs) in smart homes by proposing ADL-LLM, a system that uses large language models for zero-shot recognition, showing effectiveness on two public datasets.
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.