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

环境温度不断上升的趋势使农业等边缘化人口 经常暴露在湿热环境中的工人，急性健康风险增加 热暴露的影响，特别是热相关疾病(HRI)和脱水。HRI和脱水是 尤其是潜伏的，因为他们可以迅速从中度不适发展到混乱和受损 因此，受影响的工人寻求必要的医疗救治的能力有所下降。热暴露 调用多种生理反应模式。因此，多模式传感器和计算算法 集成来自这些传感器的数据流是必要的，以更好地了解热暴露如何导致急性 对健康的影响。我们计划开发一种无线可穿戴设备，它包含多个纳米级传感器，可以 实时集成关键生理信号，并能够预测和生成有关不良反应的警告 活动也是实时的。该项目围绕三个目标组织：1)开发一种基于纳米薄膜的柔软、 用于监测生理状况的可穿戴生物贴片，包括皮肤温度、皮肤水分、心率、 呼吸频率、血氧饱和度、运动和心电图，以及远程蓝牙 2)为新的计算算法开发了一个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，并考虑内部和内部 形态相关性为了开发与HRI症状相关联的生物标记物的预测模型， 脱水，以及脱水的生物标志物；以及3)决定这项技术在以下方面的性能 野外条件下的户外工作人员。我们将在第一年和第二年对连续的原型进行现场测试，招募 在亚特兰大地铁受雇的户外工人佩戴Biopatch和我们在我们的 其他关于职业热暴露的生理反应的研究。我们将进一步测试生物贴片在 更恶劣的条件，招募佛罗里达州的农业工人参加相同的方案，以便在第三年进行评估 和4.这项工作具有重要意义，因为我们将能够确定和集成多种模式 热暴露的生理反应，以便在HRI症状出现之前识别不良影响。创新 这项工作在于将纳米工程和计算算法相结合，以跟踪、监测和预测 工人对极端高温条件的反应。我们已经组建了一支由经验丰富的跨学科团队组成的明星团队 来自埃默里大学和佐治亚理工学院的研究人员拥有纳米工程、计算机科学方面的专业知识， 以及农业工人热生理的实地研究。这个项目的发现将引领我们走向未来 使用Biopatch向员工、好友和/或工作人员持有的Android设备发送数据的干预研究 确定使用这项技术是否可以减少与职业性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来自 建议进行一项研究，以便在员工被预测有不良后果时生成警报。

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.