用大模型整合多源可穿戴设备数据，提升应急场景下人体行为识别能力
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework
- 将不同可穿戴设备视为多维输入，构建多维度学习框架
- 大模型融合复杂传感器数据，生成可操作的决策洞察
- 适合应急救援、养老护理等高风险场景的智能系统研发者
在应急响应或老年照护等高风险环境中，大语言模型（LLM）通过整合来自各类可穿戴传感器的数据，革新了人体活动识别（HAR）系统中的风险评估、资源分配与应急响应机制。本文提出一种概念框架，将不同可穿戴设备视为单一维度，支持在HAR系统中实现多维度学习。通过整合与处理多源数据，LLM能将复杂的传感器输入转化为可行动的洞察，缓解数据固有的不确定性和复杂性，从而提升应急服务的响应速度与有效性。该研究为探索LLM在HAR系统中的变革潜力奠定基础，助力应急人员应对不可预测且高危的工作环境。
原文摘要 · Abstract (English)
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.
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