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使用光学传感器对下肢生命体征进行非侵入式监测。

Non-Intrusive Monitoring of Vital Signs in the Lower Limbs Using Optical Sensors.

作者信息

Simões Joana, Oliveira Regina, Costa Florinda M, Teixeira António, Leitão Cátia, Correia Pedro, Silva Ana Luísa M

机构信息

Institute for Nanostructures, Nanomodelling and Nanofabrication (i3N), Department of Physics, University of Aveiro, 3810-193 Aveiro, Portugal.

Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Electronics Telecommunications & Informatics, University of Aveiro, 3810-193 Aveiro, Portugal.

出版信息

Sensors (Basel). 2025 Jan 7;25(2):305. doi: 10.3390/s25020305.

Abstract

Invisible health monitoring is currently a topic of global interest within the scientific community. Sensorization of everyday objects can provide valuable health information without requiring any changes in people's routines. In this work, a feasibility study of photoplethysmography (PPG) acquisition in the lower limbs for continuous and real-time monitoring of the vital signs, including heart rate (HR) and respiratory rate (RR), is presented. The proposed system uses two MAX30102 sensors to obtain PPG signals from the back of the thigh. As proof of concept, tests were conducted in 17 volunteers (age group between 22 and 40 years old, twelve females and five males), and the results were compared to those of reference sensors. A Pearson correlation coefficient of r = 0.92 and r = 0.77 and a mean difference of 1.2 bpm and 0.9 rpm for HR and RR, respectively, were obtained between the developed system and reference. System accuracies of 95.9% for HR and 91.3% for RR were achieved, showing the system viability for vital sign monitoring of the lower limbs.

摘要

无形健康监测目前是科学界全球关注的一个话题。日常物品的传感化能够在无需人们改变日常习惯的情况下提供有价值的健康信息。在这项工作中,提出了一项关于在下肢采集光电容积脉搏波描记法(PPG)以连续实时监测生命体征(包括心率(HR)和呼吸频率(RR))的可行性研究。所提出的系统使用两个MAX30102传感器从大腿后部获取PPG信号。作为概念验证,对17名志愿者(年龄在22至40岁之间,12名女性和5名男性)进行了测试,并将结果与参考传感器的结果进行了比较。所开发系统与参考系统之间,心率的皮尔逊相关系数r = 0.92,呼吸频率的皮尔逊相关系数r = 0.77,心率和呼吸频率的平均差异分别为1.2次/分钟和0.9次/分钟。心率的系统准确率达到95.9%,呼吸频率的系统准确率达到91.3%,表明该系统在下肢生命体征监测方面具有可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44a5/11768218/9cfcc05857a7/sensors-25-00305-g001.jpg

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