基于匹配滤波的非接触生命体征测量
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1.上海交通大学人工智能教育部重点实验室 上海 200240; 2.上海交通大学医学院附属 第六人民医院心血管内科 上海 200233

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TN957.51

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上海交通大学“交大之星”医工交叉研究基金(YG2023QNA30)项目资助


Non-contact vital sign measurement based on matched filtering
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1.MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University,Shanghai 200240, China; 2.Department of Cardiovascular Medicine, Shanghai Sixth People′s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233, China

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    摘要:

    针对目前非接触生命体征测量中稳定性和精度有限且各解决方法复杂度较高的问题,设计了一种基于匹配滤波的非接触生命体征测量方法,以实现低计算复杂度的同时保持最优性估计。在办公室环境下对五个样本进行了测试,结果表明所提出方法的有效性,能降低实际环境中由于人员体动等所导致的生命体征测量误差。以样本4为例,在平稳性设计下心率方差由2 825下降至82,在精度跟踪校准设计下,心率均方根误差由16下降至4。临床实验则与现行医学参考标准进一步对比,结果表明呼吸率误差在1 bpm内,同时心率的测量结果也更好,具有潜在的实用性。

    Abstract:

    Aiming at the current problems of limited stability and accuracy and high complexity of each solution in non-contact vital signs measurement, a non-contact vital signs measurement method based on matched filtering is designed to achieve low computational complexity and maintain optimality estimation. Five samples are tested in an office environment, and the results show the effectiveness of the proposed method to reduce the vital sign measurement errors due to people′s body movements in real environments. As an example, for sample 4, the variance of heart rate decreases from 2 825 to 82 in the smoothness design, and the root mean square error of heart rate decreases from 16 to 4 in the accuracy tracking calibration design. Clinical experiments are further compared with the current medical reference standards, and the results show that the respiratory rate error is within 1 bpm, while the heart rate measurements are better, which makes it potentially useful.

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曹杰,温力,唐敏,郭宜竞,顾昌展.基于匹配滤波的非接触生命体征测量[J].电子测量技术,2025,48(1):39-45

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  • 在线发布日期: 2025-02-24
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