تقرير
Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
| العنوان: | Large Language Models are Zero-Shot Recognizers for Activities of Daily Living |
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| المؤلفون: | Civitarese, Gabriele, Fiori, Michele, Choudhary, Priyankar, Bettini, Claudio |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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. Comment: Currently under review |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.01238 |
| رقم الأكسشن: | edsarx.2407.01238 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |