通过使用知识蒸替换注意力以模式明智的卷积来进行基于PPG的高能耗心率估计,使用知识蒸
IEEE journal of biomedical and health informatics
|June 17, 2025
概括
这项研究介绍了PULSE,这是一款轻量级的深度学习模型,用于从光电脉冲图 (PPG) 传感器准确地跟踪心率 (HR),克服运动器件. 一个蒸的学生模型实现了最先进的性能,显著减少了可穿戴设备的尺寸和能耗.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 连续的心率 (HR) 监测通过光电脉学 (PPG) 对于可穿戴设备至关重要.
- 来自手臂运动的运动工件 (MA) 降低了PPG信号的准确性.
- 现有的深度学习 (DL) 融合方法对于资源有限的可穿戴设备来说往往太大了.
研究的目的:
- 开发一种新的,轻量级的深度学习架构 (PULSE),以改善人力资源跟踪中的传感器融合.
- 为在微控制器上部署高效的DL模型创建一个知识蒸机制.
- 根据最先进的方法评估拟议模型的性能和资源效率.
主要方法:
- 一个新的轻量级DL架构,PULSE,利用多头交叉注意层进行时间特征融合.
- 一种基于关系的知识蒸技术,用于训练一个较小的学生网络,使用模式智能的卷积.
- 培训后的量子化应用于微控制器部署的学生模型.
主要成果:
- 在PPG-DaLiA数据集上,PULSE实现了接近最先进的性能,并在WESAD上将平均绝对误差 (MAE) 降低了22.6%.
- 蒸的学生模型在PPG-DaLiA上显示了4.81 BPM的MAE,与最先进的状态相比.
- 学生模型实现了10.9×更低的内存占地面积 (37.9kB) 和45.9×更低的能耗 (0.577mJ).
结论:
- 拟议的PULSE架构及其提炼的学生模型为可穿戴设备中精确的HR监控提供了有效和高效的解决方案.
- 轻量化设计和减少资源需求使这些模型适合在微控制器上实时部署.
- 这项工作解决了在边缘设备上部署复杂DL算法的挑战,用于生理信号处理.
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