Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914964741029888 |
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| author | Hasan, Syed Mhamudul |
| author_facet | Hasan, Syed Mhamudul |
| contents | In high-stake environments like emergency response or elder care, the integration of large language model (LLM), revolutionize risk assessment, resource allocation, and emergency responses in Human Activity Recognition (HAR) systems by leveraging data from various wearable sensors. We propose a conceptual framework that utilizes various wearable devices, each considered as a single dimension, to support a multidimensional learning approach within HAR systems. By integrating and processing data from these diverse sources, LLMs can process and translate complex sensor inputs into actionable insights. This integration mitigates the inherent uncertainties and complexities associated with them, and thus enhancing the responsiveness and effectiveness of emergency services. This paper sets the stage for exploring the transformative potential of LLMs within HAR systems in empowering emergency workers to navigate the unpredictable and risky environments they encounter in their critical roles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03546 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework Hasan, Syed Mhamudul Human-Computer Interaction Computers and Society Machine Learning In high-stake environments like emergency response or elder care, the integration of large language model (LLM), revolutionize risk assessment, resource allocation, and emergency responses in Human Activity Recognition (HAR) systems by leveraging data from various wearable sensors. We propose a conceptual framework that utilizes various wearable devices, each considered as a single dimension, to support a multidimensional learning approach within HAR systems. By integrating and processing data from these diverse sources, LLMs can process and translate complex sensor inputs into actionable insights. This integration mitigates the inherent uncertainties and complexities associated with them, and thus enhancing the responsiveness and effectiveness of emergency services. This paper sets the stage for exploring the transformative potential of LLMs within HAR systems in empowering emergency workers to navigate the unpredictable and risky environments they encounter in their critical roles. |
| title | Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework |
| topic | Human-Computer Interaction Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2410.03546 |