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Updated: Jan 29, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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使用可穿戴传感器数据进行有效和可解释的疲劳预测的选择性RAG增强混合ML-LLM框架.

Soonho Ha1, Taeyoung Lee1, Hyungjun Seo1

  • 1Department of Medical Informatics, College of Medicine, Korea University, Seoul 02841, Republic of Korea.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
概括

一个新的混合机器学习和大型语言模型框架提高了高压力工作的疲劳分类准确性. 这种方法提高了使用可穿戴传感器数据的预测可靠性和解释性.

关键词:
可以解释的人工智能AI疲劳的预测和疲劳的预测混合推理推理是混合推理.大型语言模型 (LLM)机器学习是机器学习.可以穿戴的传感器.

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