SCH: Wireless battery-less smart sensing and analytic with application to wound assessment

SCH：无线无电池智能传感和分析，应用于伤口评估

• 批准号: 10238028
• 负责人: Matthew L Johnston
• 金额: $ 23.43万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-22 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238028
• 关键词: Acute; Address; Animal Model; Bandage; Biological Markers; Caregivers; Caring; Chronic Disease; Classification; Clinic; Clothing; Computer software; Data; Development; Devices; Disease; Early Diagnosis; Electronics; Feedback; Goals; Health; Health Status; Home; Incidence; Individual; Infection; Intelligence; Measurement; Methods; Mission; Modeling; Monitor; Morbidity - disease rate; Morphologic artifacts; National Institute of Biomedical Imaging and Bioengineering; Noise; Patient Care; Patient Education; Patients; Prognosis; Research; SMART health; Series; Signal Transduction; Sterile coverings; Surgical Wound Infection; Technology; Temperature; Testing; Textiles; Time; Validation; Wireless Technology; care providers; chronic wound; connected health; design; improved; innovation; mortality; novel; operation; sensor; sensor technology; targeted biomarker; wound; wound bed; wound care

The goal of the proposed project is to develop smart and connected health sensors using soft and conformable materials for continuous monitoring via sweat and other biofluids. Integrated into bandages, dressings, diapers, or clothing, and powered wirelessly using WiFi, these would enable continuous measurement of multiple vitals, biomarkers, and metabolites for both healthy individuals and those with acute or chronic illness. An application that embodies the critical need for such sensors is the monitoring and care of wounds, especially chronic wounds and surgical site infections. Wound monitoring in the clinic is infrequent, and patients often must self-monitor and care for their wounds at home. To address this need, this proposal describes the design, implementation, and validation of a smart wound monitor that will assess wound status through multiple sensors, including temperature, moisture, pH, and targeted biomarkers. Acquiring these continuous signals in a useful manner requires sensors that can be integrated cheaply into a bandage or dressing, including wireless data readout and battery-less operation. Extracting prognosis prediction requires robust inference from time-varying and temperature-varying sensor data, in the presence of artifacts and environmental noise, requiring connected and intelligent analytics in real time. The proposed research includes the development of conformal electrochemical sensors for measurement of biological markers on soft fabrics; development of integrated circuits that extend the range of operation of RF-powered sensors; co-integration of sensors and electronics into a disposable, battery-less device using adaptive multi-sensor readout interface circuits for energy minimization; and, the application of advanced probabilistic classification methods to multi-sensor time-series data for novel prognosis prediction and health status modeling. The proposed approach would provide multiple benefits for the patient and care provider, including (i) automated feedback to the clinician on wound progression, (ii) feedback to the patient to reduce incidence of common wound-care mistakes, and (iii) early detection of infection, reducing infection-associated morbidity and mortality. The project additionally includes a validation plan using an artificial wound bed, followed by testing in animal model, as well as the development of materials for training patients, caregivers, and clinicians in the proper use of the smart wound monitor.

拟议项目的目的是使用软和 通过汗液和其他生物流体进行连续监测的综合材料。整合到绷带中， 这些敷料，尿布或衣服，并使用WiFi无线供电，这些将使连续 针对健康个体和患有多种生物标志物和代谢物的多种生物，生物标志物和代谢物的测量 急性或慢性病。体现此类传感器的关键需求的应用程序是监视 以及伤口，尤其是慢性伤口和手术部位感染。诊所的伤口监测 很少见，患者通常必须自我监测并照顾家里的伤口。 为了满足这一需求，该建议描述了SMART的设计，实现和验证 伤口监视器将通过多个传感器评估伤口状态，包括温度，水分， pH和靶向生物标志物。以有用的方式获取这些连续信号需要传感器 可以便宜地集成到绷带或敷料中，包括无线数据读数和无电池 手术。提取预后预测需要随着时变和 在存在伪影和环境噪声的情况下，温度变化的传感器数据需要连接 和智能分析实时。拟议的研究包括共形的发展 用于测量软化织物生物标记的电化学传感器；综合的发展 扩展RF供电传感器操作范围的电路；传感器和 电子设备使用自适应多传感器读数接口电路进入一次性无电池设备 能量最小化；并且，将高级概率分类方法应用于多传感器 新的预后预测和健康状况建模的时间序列数据。 拟议的方法将为患者和护理提供者提供多个好处，包括（i） 自动反馈给临床医生有关伤口进展的自动反馈，（ii）对患者的反馈以减少发生率 常见的伤口护理错误和（iii）早期发现感染，减少了与感染相关的 发病率和死亡率。该项目还包括使用人造伤口床的验证计划， 然后在动物模型中进行测试，以及用于培训患者的材料的开发， 护理人员和临床医生在适当使用智能伤口监视器中。