A Multimodal Wearable System for Deep Space Monitoring of Stress and Anxiety

The goal of this project is to develop a holistic hardware/software solution based on a multimodal wearable sensing platform to achieve dynamic deep space stress and anxiety assessment. Sweat could serve as an excellent candidate for non-invasive stress response monitoring as it contains rich physiological information. The hypothesis is that sweat analyte levels monitored continuously along with the key vital signs, when coupled with machine learning approach, will provide accurate and dynamic stress and anxiety assessment. The approach is to simultaneously monitor the molecular analytes in human sweat including stress hormones (i.e., cortisol, adrenaline, and noradrenaline), glucose, lactate, sodium, potassium, pH, sweat rate, and key vital signs (i.e., skin temperature, blood pressure, heart rate, and heart rate viability) using the wearable multimodal sensing platform. Based on a combination of the physical/molecular data and machine learning model, a more comprehensive stress assessment system with significantly higher accuracy and robustness can be achieved.

High levels of anxiety and stress due to the extreme working environment, significantly affect the performance abilities of NASA astronauts to perform the mission. A dynamic stress-detection system would be extremely useful for the self-management of mental as well as physical health. Early detection and classification of the severity of stress followed by timely intervention are crucial factors in improving human performance. Current approaches for stress assessment are largely limited to questionnaire-based scales which can be very subjective and cumbersome to implement in the space. The data collected from our wearable devices could potentially revolutionize traditional health monitoring practices and to empower investigations of non-invasive dynamic stress assessment. Our approach will also enable numerous fundamental investigations on chronic health conditions such as depression. We envision that this technology, along with longitudinal and cross-sectional cohort studies, will significantly benefit the understanding of the health needs of individuals and society as a whole during the COVID-19 pandemic.

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