使用LLM引导的合成数据进行个性化睡眠分析的个人辅助蒸框架,使用紧型模型进行个人化睡眠分析
Huimin Zheng1, Xingxing Ai2, Xueyan Liu3
1School of Electronics and Information, South China University of Technology, Guangzhou, China.
我们开发了一个用于设备上的睡眠分析的框架,使用了新的数据合成和模型蒸技术. 这使得边缘设备的个性化健康见解能够实现,尽管数据稀缺和计算限制.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 边缘设备上的个性化睡眠分析对于实时健康洞察至关重要,但受到有限的数据和硬件限制的阻碍.
- 现有的方法与数据稀缺以及边缘部署的计算需求作斗争.
研究的目的:
- 在边缘设备上创建个性化睡眠分析框架.
- 为了应对有限的生理数据和有限的模型容量所带来的挑战.
主要方法:
- 开发了一种生理上受约束的适应性层次性合体方法,用于合成现实的生理信号.
- 引入了专家推断的配置辅助蒸与MoE LoRA,以在资源有限的模型上进行增强的个性化推断.
主要成果:
- 蒸模型的性能与最先进的大型语言模型相美.
- 模型在边缘设备计算和内存限制范围内有效运行.
- 在公共和内部数据集上都表现出有效性.
结论:
- 拟议的框架为设备上个性化睡眠分析提供了一个实际的解决方案.
- 它在资源有限的环境中弥合了高性能建模和实时医疗保健应用之间的差距.
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