NeoWear: An IoT-connected e-textile wearable for neonatal medical monitoring
NeoWear: An IoT-connected e-textile wearable for neonatal medical monitoring
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DOI：
10.1016/j.pmcj.2022.101679
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发表时间：
2022-09-05
影响因子：
4.3
通讯作者：
Mankodiya, Kunal
中科院分区：
文献类型：
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作者：
Cay, Gozde;Solanki, Dhaval;Mankodiya, Kunal
据世卫组织统计，全球每年有1500万婴儿早产。早产儿（在妊娠37周之前出生）与足月出生的婴儿（约37周）相比，其内科和外科发病率的风险明显更高。随着早产儿数量的增加，需要创新的解决方案来满足新生儿重症监护室（NICU）不断增长的需求。在NICU中监测各种生命体征，如心率（HR）、呼吸率（RR）或血氧水平（SpO2）。考虑到肺部在妊娠的最后几周发育的事实，NICU中的早产儿需要复杂的技术来监测呼吸和与呼吸相关的事件。当前技术依赖于胸阻抗的间接测量或其他侵入性技术来进行RR监测。这会给婴儿带来不适和感染的风险。此外，父母和临床护理的提供在很大程度上受到放置在婴儿身上的大量监控电缆的影响。为了满足这一要求，我们开发了一种基于物联网（IoT）的智能纺织胸带，名为“NeoWear”，用于监测婴儿的RR和检测呼吸暂停事件。NeoWear是一个可穿戴系统，由传感器腰带、可穿戴嵌入式系统和边缘计算设备组成。传感器带由智能纺织品制成的压力传感器和惯性测量单元（IMU）组成，用于监测运动。这些传感器连接到配备有无线通信能力的微控制器，称为无线嵌入式系统（WES）。WES使用基于MQTT的物联网网络架构与边缘计算设备（ECD）无线连接。ECD能够提供信号处理和计算服务，以检测RR和呼吸暂停事件。使用高保真可编程NICU婴儿人体模型和五名健康成人进行模拟实验，以测试NeoWear系统的有效性。我们的研究结果显示，呼吸率测量的平均误差为0.89 BrPM，婴儿人体模型呼吸暂停检测的准确率为97%。我们的实验还证明了婴儿人体模型的运动如何影响呼吸信号在呼吸暂停发作和呼吸频率增加到每分钟40次呼吸时。此外，人类呼吸数据的变化显示，缓慢、正常和快速呼吸的变化有意义。我们还计算了计算和通信延迟，发现它们分别类似于66和22 ms。我们的初步结果显示了NeoWear测量婴儿呼吸和相关事件的有效性。(c)2022 Elsevier B.V.保留所有权利。
According to WHO, 15 millions babies are born preterm each year globally. Preterm infant (born before 37 weeks of gestation) are at a significantly higher risk of medical and surgical morbidity in comparison to babies born at term (around 37 weeks). Innovative solutions are warranted to meet the increased requirements of Neonatal Intensive Care Unit (NICU) with rising number of preterm babies. Various kinds of vital signs such as heart rate (HR), respiration rate (RR) or blood oxygen level (SpO2) are monitored in NICU. Considering the fact the lungs develop in the last few weeks of gestation, preterm babies in NICU demands sophisticated technology to monitor respiration and events related to the respiration. Current technologies rely on the indirect measurements from thoracic impedance or other invasive techniques for RR monitoring. This poses discomfort and risk of infections to babies. Also, the delivery of parental and clinical care is largely impacted by a large number of monitoring cables placed on the babies. To address this requirements, we have developed an Internet-of-Things (IoT) based smart textile chest belt called "NeoWear"to monitor RR and detect apnea events in babies. The NeoWear is a wearable system consisting of a sensor belt, a wearable embedded system, and an edge computing device. The sensor belt comprised of a pressure sensors made of smart-textile and an Inertial Measurement Unit (IMU) to monitor movements. These sensors are connected to a micro-controller equipped with wireless communication capabilities called as a wireless embedded system (WES). The WES wirelessly connects with an edge computing device (ECD) using an MQTT-based IoT networking architecture. ECD is capable to offer signal processing and computing services to detect RR and apnea events. Simulation experiments using a high-fidelity, programmable NICU baby mannequin and five healthy adults were conducted to test the efficacy of the NeoWear System. Our findings shows an average error of 0.89 BrPM in respiration rate measurement and similar to 97 percent accuracy in apnea detection on baby mannequin. Our experiments also demonstrated that how the movements of the baby mannequin affected the respiration signal during apnea episodes and when breathing rate increased to 40 breaths per minute. In addition, the changes on human respiration data showed a meaningful increase for slow, normal and fast breathing. We also computed computation and communication latencies and they were found to be similar to 66 and 22 ms, respectively. Our preliminary results are promising showing the efficacy of NeoWear to measure respiration and related events for babies.(c) 2022 Elsevier B.V. All rights reserved.