Large Language Models are Zero-Shot Recognizers for Activities of Daily Living
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913749009432576 |
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| author | Civitarese, Gabriele Fiori, Michele Choudhary, Priyankar Bettini, Claudio |
| author_facet | Civitarese, Gabriele Fiori, Michele Choudhary, Priyankar Bettini, Claudio |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_01238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models are Zero-Shot Recognizers for Activities of Daily Living Civitarese, Gabriele Fiori, Michele Choudhary, Priyankar Bettini, Claudio Artificial Intelligence Computation and Language Signal Processing 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. |
| title | Large Language Models are Zero-Shot Recognizers for Activities of Daily Living |
| topic | Artificial Intelligence Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2407.01238 |