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.

Environmental heat exposure can have significant health consequences. In particular agricultural workers experience heat-related mortality at a rate 35 times greater than that of the overall US workforce. Through this study we aim to develop biopatch and associated software that can process signals from multiple sensors to generate warning alerts prior to development of symptoms of heat-related illness or dehydration.