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基于智能手机的健康监测在印度:数据收集和评估脉冲率估计的数据收集和评估.

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  • 1Department of Community Medicine, Father Muller Medical College, Mangalore, Karnataka, India.

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概括

智能手机摄像头可以准确地估计脉率,使用光电脉图 (PPG) 信号,为远程健康监测提供了可行的解决方案,特别是在多样化的印度人口中.

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机器学习 机器学习氧度计可以测量氧度.摄影复合发电图 (Photoplethysmogram) 是一种摄影图谱.脉冲率是指心脏的脉冲率.智能手机的智能手机智能手机的智能手机.

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科学领域:

  • 生物医学工程 生物医学工程
  • 数字健康数字健康
  • 心血管监测 心血管监测

背景情况:

  • 远程生命体征监测变得越来越重要,COVID-19大流行加快了这一点.
  • 智能手机和可穿戴设备提供了可访问,具有成本效益的健康监测解决方案.
  • 开发精确的生命估计算法需要大量,多样化的数据集,这些数据很少,特别是在印度的环境中.

研究的目的:

  • 开发一个多样化的数据集,用于印度的生命体征估计.
  • 使用此数据集,评估基于智能手机的脉冲率估计的准确性.

主要方法:

  • 收集了印度患者的数据,这些患者具有不同的人口统计和健康状况.
  • 开发了一种算法来估计来自智能手机视频录制的光电脉冲图 (PPG) 信号.
  • 从PPG数据计算脉冲率,并将其与脉冲氧计读数进行比较.

主要成果:

  • 与脉冲氧计相比,基于智能手机的PPG算法实现了相当准确的脉冲率估计 (MAE <5,ICC >0.90).
  • 准确性在各种主题和医疗保健环境中保持一致.

结论:

  • 基于智能手机的PPG信号包含足够的信息,可用于可靠的脉冲率估计.
  • 量身定制的数据集收集对于开发和验证不同人群的重要估计算法至关重要.