混合感应-LLM:一种结构化的多模式框架,用于从具有上下文自我报告的可穿戴传感器进行基于大型语言模型的健康预测
Cheng-Huan Yu1, Mohammad Masum1
1Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
混合感应器使用可穿戴传感器数据和大型语言模型准确预测健康指标,如压力和睡眠质量. 该框架提供了一种可扩展和透明的方法来解释消费者可穿戴数据.
科学领域:
- 数字健康数字健康
- 人工智能的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 可穿戴传感器收集连续的生理数据,但健康预测受到数据噪声和不规则采样的阻碍.
- 现有的方法与主观的健康结果作斗争.
研究的目的:
- 引入HybridSense,这是一个统一的框架,用于从可穿戴数据中准确和可解释的健康预测.
- 将原始可穿戴信号和统计特征与大型语言模型 (LLM) 推理集成.
主要方法:
- 利用PMData数据集,将分钟心率和活动日志转化为每日统计特征.
- 使用随机森林模型对特征相关性进行排名.
- 嵌入式功能和波形细分为结构化的提示,用于在七个提示策略和三个LLM家族 (OpenAI 4o-mini,Gemini 2.0 Flash,DeepSeek聊天) 中进行评估.
主要成果:
- 通过引导分析证明了强大的,依赖于任务的性能.
- 发现零射击提示对于疲劳和压力是最佳的,而少射击提示改善了睡眠质量估计.
- 展示了HybridSense通过将描述符与波形上下文结合起来来增强准备预测的能力,以及通过自我一致性和思想树提示来改善可变目标的稳定性.
结论:
- 快速驱动的LLM推理,结合可解释的信号特征,为健康预测提供了一个可扩展和透明的方法.
- 混合感应框架为消费者可穿戴数据分析提供实用的延迟和低推断成本.
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