Sensor Hardware and Intelligent Tools for Assessing the Health Effects of Heat Exposure

用于评估热暴露对健康影响的传感器硬件和智能工具

• 批准号: 10522560
• 负责人: VICKI Stover HERTZBERG
• 金额: $ 63.66万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-13 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522560
• 关键词: Accounting; Acute; Adverse effects; Adverse event; Affect; Agricultural Workers; Android; Biological Markers; Biomedical Engineering; Biosensor; Blood; Bluetooth; Body Temperature; Chronic; Computational algorithm; Computer software; Confusion; Data; Data Science; Dehydration; Detection; Development; Devices; Dizziness; Electrocardiogram; Electrolytes; Environment; Epidemiology Nursing; Equipment; Evaluation; Exposure to; Feedback; Feeds; Florida; Future; Generations; Goals; Headache; Health; Heart Rate; Heat Stress Disorders; Hydration status; Immigrant; Impairment; Individual; Intelligence; Intervention; Intervention Studies; Judgment; Latin America; Lead; Learning; Machine Learning; Measures; Methods; Modality; Modeling; Monitor; Morbidity - disease rate; Motion; Muscle Cramp; Nanomanufacturing; Nature; Nausea; Occupational; Occupational Exposure; Outcome; Oxygen; Pattern; Performance; Persons; Physiologic Monitoring; Physiological; Physiology; Printing; Process; Protocols documentation; Public Health; Reporting; Research Personnel; Risk; Signal Transduction; Skin; Skin Temperature; Software Tools; Sweating; Symptoms; Syncope; System; Technology; Temperature; Testing; Time; Universities; Vomiting; Weather; Work; adverse outcome; base; climate change; computer science; data streams; evaporation; experience; extreme heat; field study; high risk; innovation; machine learning algorithm; marginalized population; medical attention; medically necessary care; mortality; multi-task learning; multidisciplinary; multimodality; nanoengineering; nanomaterials; nanomembrane; nanoscale; network models; novel; predictive modeling; prevent; privacy preservation; prototype; real world application; recruit; respiratory; response; sensor; shift work; therapy development; tool; trend; wireless

Escalating trends of increasing environmental temperatures place marginalized populations such as agricultural workers, who have routine occupational exposure to hot, humid environments, at increased risk for acute health effects of heat exposure, in particular heat-related illness (HRI) and dehydration. HRI and dehydration are particularly insidious as they can quickly progress from moderate discomfort to confusion and impaired judgement, thereby diminishing the affected worker’s ability to seek necessary medical attention. Heat exposure invokes multiple modes of physiologic response. Thus, multi-modal sensors and computational algorithms to integrate data streams from these sensors are necessary to better understand how heat exposure leads to acute health effects. We propose to develop a wireless wearable unit containing multiple nanoscale sensors that can integrate key physiological signals in real-time and with the ability to predict and generate warning about adverse events also in real-time. This project is organized around three aims: 1) develop a soft, nanomembrane-based, wearable biopatch for monitoring physiological conditions, including skin temperature, skin hydration, heart rate, respiratory rate, blood oxygen saturation, motion, and electrocardiogram, along with a long-range Bluetooth transmitter; 2) develop a multi-sensor multi-task learning framework for novel computational algorithms using machine learning to integrate sensor data in real-time, extracting features and accounting for inter- and intra- modality correlations in order to develop predictive models for biomarkers associated with HRI symptoms, dehydration, and biomarkers of dehydration; and 3) determine performance of this technology when used by outdoor workers in field conditions. We will field test successive prototypes throughout years 1 and 2, recruiting outdoor workers employed in metro Atlanta to wear both a biopatch and other gear that we have used in our other studies of physiological responses to occupational heat exposure. We will further test the biopatch under more rugged conditions, recruiting Florida agricultural workers for the same protocol for evaluation in years 3 and 4. This work is significant in that we will be able to determine and integrate the multiple modalities of physiologic response to heat exposure to recognize adverse effects before HRI symptom onset. Innovation of the work lies in the integration of nanoengineering and computational algorithms to track, monitor, and predict worker response to extreme heat conditions. We have assembled a stellar interdisciplinary team of experienced investigators from Emory University and Georgia Tech with expertise in nanoengineering, computer science, and field studies of heat physiology in agricultural workers. Findings from this project will lead to a future intervention study using the biopatch to send data to Android devices held by the worker, a buddy, and/or crew chief to determine if using this technology can reduce morbidity associated with occupational heat exposure. Real-world application of the HRI detection and alert system will rely on machine learning algorithms from the proposed study to generate alerts if the worker is predicted to have an adverse outcome.

不断提高的环境温度升级的趋势将边缘化人口（例如农业） 工人常规的职业接触炎热，潮湿的环境，急性健康风险增加 热暴露的影响，特别是与热有关的疾病（HRI）和脱水的影响。 HRI和脱水是 特别是阴险的，因为它们可以迅速从现代不适转变为困惑和障碍 判断，从而减少了受影响工人寻求必要医疗护理的能力。热暴露 调用多种物理学反应模式。那是多模式传感器和计算算法 这些传感器的集成数据流是必要的，以更好地了解热暴露如何导致急性 健康影响。我们建议开发一个无线可穿戴单元，该单元包含多个纳米级传感器 实时集成关键的生理信号，并能够预测和生成有关广告的警告 活动也实时。该项目围绕三个目的组织：1）开发一个柔软的基于纳米膜的， 可穿戴的生物opatch监测生理状况，包括皮肤温度，皮肤水合，心率， 呼吸频率，血氧饱和度，运动和心电图以及远距离蓝牙 发射机; 2）使用新型计算算法开发多传感器多任务学习框架 机器学习以实时集成传感器数据，提取特征并考虑到间和内部 模态相关性是为了开发与HRI符号相关的生物标志物的预测模型， 脱水和脱水的生物标志物； 3）确定该技术的性能。 在现场条件下的户外工人。我们将在1年和2年内进行现场测试成功的原型 亚特兰大雇用的户外工人在我们的 其他对职业热暴露的身体反应的研究。我们将进一步测试在 更加坚固的条件，招募佛罗里达农业工作者进行同一协议，以评估3年级 和4。这项工作很重要，因为我们将能够确定和整合多种方式 对热暴露的生理反应，以识别HRI符号发作之前的不良反应。创新 这项工作在于纳米工程和计算算法的集成，以跟踪，监视和预测 工人对极端热量条件的反应。我们组建了一个出色的跨学科经验团队 埃默里大学和佐治亚理工学院的研究人员具有纳米工程，计算机科学专业知识， 以及农业工作者热生理学的现场研究。该项目的发现将导致未来 干预研究使用BiooOpatch将数据发送到工人，好友和/或机组人员持有的Android设备 负责确定使用此技术是否可以减少与职业热暴露相关的发病率。 HRI检测和警报系统的现实应用程序将依赖于机器学习算法 拟议的研究是否会产生警报，如果预计工人会有不良结果。