一种基于ML的方法,在运动器件的存在下,从PPG重建心率
José María Vicente-Samper1, Christian Tamantini2, Ernesto Ávila-Navarro3
1Neuroengineering Biomedical Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain.
Biosensors
|July 28, 2023
概括
这项研究开发了一个机器学习算法和一个专门的腕带,以准确监测患有自闭症谱系障碍 (ASD) 的人的心率 (HR),即使在活动中也会导致传统光电显微镜 (PPG) 信号中的运动工件.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 自闭症谱系障碍研究研究
背景情况:
- 心率监测对于评估生理状态至关重要.
- 摄影电磁镜 (PPG) 常用于门诊HR监测,但易受运动器件的影响.
- 在患有自闭症谱系障碍 (ASD) 的人群中监测HR是具有挑战性的,因为设备不坚持和运动干扰.
研究的目的:
- 开发一种机器学习方法来重建准确的人力资源信号.
- 创建一个专门的腕带,以改善ASD患者的设备附着和舒适度.
- 为了克服日常活动中手臂,手腕和手指运动的信号损坏.
主要方法:
- 为患有自闭症的人设计和开发了一种新的腕带.
- 进行了一项实验,用户执行日常活动,以收集信号数据.
- 应用了机器学习算法,从收集的数据中重建可靠的HR信号.
主要成果:
- 开发的系统证明了获得可靠的人力资源值的能力.
- 这种方法成功地解决了密集的手腕运动引起的信号损坏问题.
- 专用腕带有助于在目标人群中更好地安装设备.
结论:
- 拟议的机器学习方法和专门的腕带有效地使得ASD患者的HR监控能够准确.
- 这项技术可以在涉及大量运动的活动期间改善ASD患者的生理监测.
- 这些发现有助于更好地理解和管理神经分化种群中的生理数据.
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