使用可穿戴惯性传感器和深度学习进行血压液态压力校正的一种方法
David A M Colburn1, Terry L Chern1, Vincent E Guo1
1Department of Biomedical Engineering, Columbia University, New York, NY 10027 USA.
精确的无袖血压 (BP) 监测受到水静压的挑战. IMU-Track使用可穿戴惯性传感器和深度学习来纠正手臂位置,改善日常运动中BP测量准确度.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 心血管健康 心血管健康
背景情况:
- 无袖非侵入性血压 (BP) 测量提供了早期检测异常BP模式的潜力.
- 传感器远离心脏水平的位置,例如在手臂上,由于水静压变化,会损害血压测量的准确性.
- 传统的水静电压力校正方法涉及重的液体填充管道,限制了不露面性.
研究的目的:
- 引入IMU-Track,这是一个替代方法来纠正无袖BP测量的水静压变化.
- 为了使准确,不引人注目的血压监测使用可穿戴传感器放置远离心脏水平.
- 通过各种参与者在现实环境中验证IMU-Track的有效性.
主要方法:
- 开发了IMU-Track,这是一个深度学习模型,分析可穿戴惯性传感器的运动数据.
- 使用IMU-Track计算参数化手臂姿势坐标,用于放置在手臂上的传感器.
- 集成的IMU-Track与来自心电图和手指光电心电图的血压测量.
主要成果:
- IMU-Track显著降低了静脉血压的绝对平均误差,从13.5 ± 1.1 mmHg降至5.9 ± 0.7 mmHg,而静脉血压则从15.0 ± 1.0 mmHg降至6.8 ± 0.5 mmHg,在心脏水平以下25厘米处.
- 在心脏水平以上25厘米时,对于静缩和静缩血压均观察到类似的误差减少.
- 在商用智能手机上,手臂跟踪推断时间约为134毫秒,适合实时校正.
结论:
- IMU-Track有效地纠正无袖BP测量的水静压变化.
- 这种方法可以在远离心脏水平的位置进行准确的被动无袖血压监测.
- IMU-Track支持开发不显眼的可穿戴血压监测器,用于持续的健康跟踪.
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