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: 10703469
• 负责人: VICKI Stover HERTZBERG
• 金额: $ 61.21万
• 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/10703469
• 关键词: Accounting; Acute; Adverse effects; Adverse event; Affect; Agricultural Workers; Android; Assessment tool; Biological Markers; Biomedical Engineering; Biosensor; Blood; Bluetooth; Body Temperature; Chronic; Computational algorithm; Computer software; Confusion; Data; Data Science; Dehydration; Detection; Development; Devices; Discipline of Nursing; Dizziness; Electrocardiogram; Electrolytes; Environment; Epidemiology; 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; 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; climate change; computer science; data integration; data streams; evaporation; experience; extreme heat; field study; health assessment; 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; transmission process; trend; wearable device; 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.

环境温度升高的趋势不断加剧，使农业等边缘化人口 经常职业暴露于炎热、潮湿环境的工人，急性健康风险更高 热暴露的影响，特别是热相关疾病 (HRI) 和脱水。 特别阴险，因为他们可以迅速从中度不适发展到混乱和受损 判断力，从而削弱受影响工人寻求必要的医疗护理的能力。 因此，多模态传感器和计算算法可以调用多种生理反应模式。 集成来自这些传感器的数据流对于更好地了解热暴露如何导致急性损伤是必要的。 我们建议开发一种包含多个纳米级传感器的无线可穿戴设备。 实时整合关键生理信号，并能够预测和生成不良警报 该项目围绕三个目标组织：1）开发一种基于软纳米膜的、 用于监测生理状况的可穿戴生物贴片，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，以及远程蓝牙 2）使用新型计算算法开发多传感器多任务学习框架 机器学习实时集成传感器数据，提取特征并考虑内部和外部 模态相关性，以开发与 HRI 症状相关的生物标志物的预测模型， 脱水和脱水的生物标志物；以及 3) 确定该技术在使用时的性能 我们将在第一年和第二年对连续的原型进行现场测试，并招募人员。 亚特兰大都会区雇用的户外工作人员佩戴生物贴片和我们在工作中使用的其他装备 对职业热暴露的生理反应的其他研究我们将进一步测试生物贴片。 条件更加恶劣，招募佛罗里达州农业工人进行相同的协议，三年内进行评估 4. 这项工作意义重大，因为我们将能够确定并整合多种模式 对热暴露的生理反应，以在 HRI 症状出现之前识别不良反应。 这项工作在于整合纳米工程和计算算法来跟踪、监测和预测 我们组建了一支经验丰富的跨学科团队。 来自埃默里大学和佐治亚理工学院的研究人员在纳米工程、计算机科学、 以及农业工人热生理学的实地研究将引领未来。 使用生物贴片将数据发送到工人、好友和/或工作人员持有的 Android 设备的干预研究 首席科学家确定使用该技术是否可以降低与职业热暴露相关的发病率。 HRI 检测和警报系统的实际应用将依赖于机器学习算法 拟议的研究是在预计工人出现不良结果时发出警报。