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使用大型语言模型对传感器数据进行短拍优化:关于疲劳检测的案例研究.

Elsen Ronando1,2, Sozo Inoue1

  • 1Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu Ward, Kitakyushu 808-0135, Japan.

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概括
此摘要是机器生成的。

我们开发了使用大型语言模型 (HED-LM) 的混合欧几里德距离,以便在基于传感器的分类中更好地进行示例选择. 通过将数字相似性与LLMs的上下文相关性相结合,HED-LM提高了疲劳检测的准确性.

关键词:
在HED-LM中使用.加速度计的加速度计.语境推理 语境推理 语境推理欧几里德距离是什么意思例如选择选择的例子.疲劳检测 疲劳检测 疲劳检测几次射击提示提示的提示大型语言模型 (LLM)传感器数据 传感器数据

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科学领域:

  • 机器学习 机器学习
  • 信号处理 信号处理
  • 可穿戴技术可穿戴技术

背景情况:

  • 对于有限的标记数据,Few-shot提示是有效的,但对示例选择质量敏感.
  • 基于传感器的分类任务,如疲劳检测,由于复杂的数据模式和可变性,需要细微的示例选择.

研究的目的:

  • 引入一种新的几拍优化方法,即混合欧几里德距离与大语言模型 (HED-LM),以改进示例选择.
  • 通过优化训练示例的选择,提高基于传感器的分类任务的性能,特别是疲劳检测.

主要方法:

  • HED-LM采用混合管道:使用欧几里德距离过候选示例,并根据大型语言模型 (LLM) 的上下文相关性得分重新排名它们.
  • 该方法在使用加速度计数据的疲劳检测任务上得到了验证,该方法以重叠模式和高个体间变异性而闻名.

主要成果:

  • 在疲劳检测中,HED-LM 实现了 69.13 ± 10.71% 的平均宏观 F1 评分.
  • 这显著优于随机选择 (59.30 ± 10.13%) 和距离仅过 (67.61 ± 11.39%),相对改善分别为16.6%和2.3%.

结论:

  • 将数值相似性 (欧几里德距离) 与上下文相关性 (LLM) 结合起来,可以提高几次拍摄提示的稳定性.
  • HED-LM为现实世界基于传感器的学习提供了实用方法,并且在医疗监测,活动识别和工业安全方面具有潜力.