Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), Institute of Bioinformatics and Systems Biology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Institute of Electrical and Control Engineering, Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Biosensors (Basel). 2022 Nov 3;12(11):964. doi: 10.3390/bios12110964.
This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (), respiration rate (), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of is lower than 1.41%, is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including , , and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic.
本研究提出了一种长期的生命体征感应长袍，由两部分组成：一个微型化的监测设备和一个智能计算平台。生命体征是表明人体功能状态的信号。通过监测生命体征，通常包括血压、体温、心率和呼吸率，可以评估一个人的一般身体健康状况。微型化监测设备由一个可以获取两种生理信号的紧凑电路组成，包括生物电势和皮肤表面温度。这两种信号在电路中进行预处理，并使用三种算法传输到智能计算平台进行进一步分析，这三种算法包括 R 波检测、心电图衍生呼吸和核心体温估计。处理后，衍生的生命体征将显示在便携式设备屏幕上，包括心电图信号、心率（）、呼吸率（）和核心体温。在临床实践中进行了验证智能计算平台性能的实验。该研究招募了 31 名参与者（10 名健康参与者和 21 名临床患者）。结果表明，在健康参与者和临床患者试验中，的相对误差低于 1.41%，的相对误差低于 5.52%，核心体温的偏差低于 0.04°C。在本研究中，微型化监测设备和三种算法（包括、和核心体温）被集成到生命体征感应长袍中。与常用的临床设备相比，该感应长袍在可用性和价格方面具有优势。使用算法估计生命体征是一种连续的、非侵入性的方法，它可能是家庭护理和临床监测的一种新颖且有潜力的设备，特别是在大流行期间。
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