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Project Title:  A Multimodal Wearable System for Deep Space Monitoring of Stress and Anxiety Reduce
Fiscal Year: FY 2021 
Division: Human Research 
Research Discipline/Element:
TRISH--TRISH 
Start Date: 04/01/2020  
End Date: 03/31/2022  
Task Last Updated: 02/25/2021 
Download report in PDF pdf
Principal Investigator/Affiliation:   Gao, Wei  Ph.D. / California Institute of Technology 
Address:  1200 E California Blvd 
MC 138-78 
Pasadena , CA 91125 
Email: weigao@caltech.edu 
Phone: 858-784-1396  
Congressional District: 27 
Web:  
Organization Type: UNIVERSITY 
Organization Name: California Institute of Technology 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Yue, Yisong  Ph.D. California Institute of Technology 
Project Information: Grant/Contract No. NNX16AO69A-T0501 
Responsible Center: TRISH 
Grant Monitor:  
Center Contact:   
Unique ID: 13949 
Solicitation / Funding Source: 2020 TRISH BRASH1901: Translational Research Institute for Space Health (TRISH) Biomedical Research Advances for Space Health 
Grant/Contract No.: NNX16AO69A-T0501 
Project Type: GROUND 
Flight Program:  
TechPort: Yes 
No. of Post Docs:
No. of PhD Candidates:
No. of Master's Candidates:
No. of Bachelor's Candidates:
No. of PhD Degrees:
No. of Master's Degrees:
No. of Bachelor's Degrees:
Human Research Program Elements: None
Human Research Program Risks: None
Human Research Program Gaps: None
Task Description: 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.

Research Impact/Earth Benefits: Despite the urgent need for the real-time monitoring of stress, current approaches for stress assessment are largely limited to questionnaire-based scales or self-reported measures, which can be very subjective and cumbersome to implement in the space. To our knowledge, this work will be the first dynamic stress assessment using a single wearable sensing patch to continuously collect both physical vital signals and molecular data from the human body. While most other designs target single, specific sensing modalities, the proposed platform is explicitly multimodal with capabilities to simultaneously monitor glucose, lactate, Na+, K+, pH, stress hormones, sweat rate, pulse waveform, and skin temperature, with opportunities in future research to incorporate many more modalities. Large data sets of molecular data collection from human trials coupled with modern machine-learning model approaches will generate an optimal algorithm to assess stress levels dynamically. Such non-invasive dynamic multimodal monitoring technology also enables numerous other fundamental investigations to address the performance, mental health, and protection of astronauts in deep space environments.

As COVID-19 became a major challenge for us over the past year, the SARS-CoV-2 RapidPlex, developed based on target-specific immunoassays built off laser-engraved graphene, could be used for rapid and remote assessment of COVID-19 biomarkers (i.e., nucleocapsid protein, anti-spike protein IgG and IgM, and C-reactive protein). The SARS-CoV-2 RapidPlex has the potential to quickly and effectively triage patients and track infection progression, allowing for the clear identification of individuals who are infectious, vulnerable, and/or immune. Modification of our platform design may allow for rapid viral antigen and antibody panel testing such that COVID-19 infection could be clearly distinguished from non-specific symptoms of seasonal respiratory infections such as influenza. Additionally, the wireless telemedicine diagnostic platform, when coupled with emerging wearable biosensors to continuously monitor vital signs and other chemical biomarkers, could provide comprehensive information on an individual's health status during the COVID-19 pandemic.

Task Progress & Bibliography Information FY2021 
Task Progress: The original 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 approaches, will provide accurate and dynamic stress and anxiety assessment.

During Year 1, we have successfully developed most of the individual sensors with high performance (including glucose, lactate, Na, K, pH, blood pulse wave, temperature, galvanic skin response (GSR)) for continuous monitoring. With materials and chemistry innovation, we have made major progress to improve the long term stability of our wearable chemical sensors (most of these sensors now have stable performance during multiple-day in vitro operation). We also developed a portable cortisol sensor for rapid stress hormone analysis in human sweat. Our engineering development in Year 1 paves the way for an integrated multimodal system and now we have integrated all these sensors into one multimodal wearable system for physical and chemical sensing on human subjects. We are trying to simultaneously monitor the molecular analytes in human sweat including stress hormone, glucose, lactate, sodium, potassium, pH, and key vital signs (i.e., skin temperature, pulse wave, GSR) using this wearable multimodal sensing platform. In the coming year, we plan to mainly focus on system validation and human studies -- evaluating the multimodal sensor performance in human subjects under different stressors and we will use machine learning to classify the stress levels based on the data collected. Based on a combination of the physical/molecular data and machine learning model, we anticipated that a more comprehensive stress assessment system with significantly higher accuracy and robustness can be achieved.

As COVID-19 became a major challenge for us over the past year, we have also developed, partially supported by the Translational Research Institute for Space Health (TRISH), an ultrasensitive and low-cost telemedicine platform, the SARS-CoV-2 RapidPlex, based on target-specific immunoassays built off laser-engraved graphene for rapid and remote assessment of COVID-19 biomarkers (i.e., nucleocapsid protein, anti-spike protein IgG and IgM, and C-reactive protein). We successfully demonstrated the platform's applicability using COVID-19-positive and COVID-19-negative serum and saliva samples. The SARS-CoV-2 RapidPlex has the potential to quickly and effectively triage patients and track infection progression, allowing for the clear identification of individuals who are infectious, vulnerable, and/or immune.

Bibliography: Description: (Last Updated: 01/19/2023) 

Show Cumulative Bibliography
 
Articles in Peer-reviewed Journals Lukas H, Xu C, Yu Y, Gao W. "Emerging telemedicine tools for remote COVID-19 diagnosis, monitoring, and management." ACS Nano. 2020 Dec 22;14(12):16180-93. https://doi.org/10.1021/acsnano.0c08494 ; PMID: 33314910; PMCID: PMC7754783 , Dec-2020
Articles in Peer-reviewed Journals Min J, Sempionatto JR, Teymourian H, Wang J, Gao W. "Wearable electrochemical biosensors in North America." Biosens Bioelectron. 2021 Jan 15;172:112750. Epub 2020 Oct 26. https://doi.org/10.1016/j.bios.2020.112750 ; PMID: 33129072 , Jan-2021
Articles in Peer-reviewed Journals Song Y, Min J, Yu Y, Wang H, Yang Y, Zhang H, Gao W. "Wireless battery-free wearable sweat sensor powered by human motion." Sci Adv. 2020 Sep 30;6(40):eaay9842. https://doi.org/10.1126/sciadv.aay9842 ; PMID: 32998888; PMCID: PMC7527225 , Sep-2020
Articles in Peer-reviewed Journals Torrente-Rodríguez RM, Lukas H, Tu J, Min J, Yang Y, Xu C, Rossiter HB, Gao W. "SARS-CoV-2 RapidPlex: A graphene-based multiplexed telemedicine platform for rapid and low-cost COVID-19 diagnosis and monitoring." Matter. 2020 Dec 2;3(6):1981-98. Epub 2020 Oct 5. https://doi.org/10.1016/j.matt.2020.09.027 ; PMID: 33043291; PMCID: PMC7535803 , Dec-2020
Awards Gao W. "American Chemical Society Nano Rising Star Lecture, May 2020." May-2020
Awards Gao W. "Biocom Life Science Catalyst Award, December 2020." Dec-2020
Awards Gao W. "Chemical Society Review 2020 Emerging Investigator, June 2020." Jun-2020
Awards Gao W. "Highly Cited Researcher 2020 by Clarivate Web of Science, December 2020." Dec-2020
Awards Gao W. "Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine & Biology Society Early Career Achievement Award, June 2020." Jun-2020
Awards Gao W. "Institute of Electrical and Electronics Engineers (IEEE) Senior Member, August 2020." Aug-2020
Awards Gao W. "MINE 2020 Young Scientist Award (by Microsystems & Nanoengineering), July 2020." Jul-2020
Awards Gao W. "World Economic Forum Young Scientist, May 2020." May-2020
Project Title:  A Multimodal Wearable System for Deep Space Monitoring of Stress and Anxiety Reduce
Fiscal Year: FY 2020 
Division: Human Research 
Research Discipline/Element:
TRISH--TRISH 
Start Date: 04/01/2020  
End Date: 03/31/2022  
Task Last Updated: 07/23/2020 
Download report in PDF pdf
Principal Investigator/Affiliation:   Gao, Wei  Ph.D. / California Institute of Technology 
Address:  1200 E California Blvd 
MC 138-78 
Pasadena , CA 91125 
Email: weigao@caltech.edu 
Phone: 858-784-1396  
Congressional District: 27 
Web:  
Organization Type: UNIVERSITY 
Organization Name: California Institute of Technology 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Yue, Yisong  Ph.D. California Institute of Technology 
Project Information: Grant/Contract No. NNX16AO69A-T0501 
Responsible Center: TRISH 
Grant Monitor:  
Center Contact:   
Unique ID: 13949 
Solicitation / Funding Source: 2020 TRISH BRASH1901: Translational Research Institute for Space Health (TRISH) Biomedical Research Advances for Space Health 
Grant/Contract No.: NNX16AO69A-T0501 
Project Type: GROUND 
Flight Program:  
TechPort: Yes 
No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:  
No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees:  
Human Research Program Elements: None
Human Research Program Risks: None
Human Research Program Gaps: None
Task Description: The goal of this research is to develop a holistic hardware/software solution based on a multimodal wearable sensing platform to achieve dynamic deep space stress and anxiety assessment. High levels of stress, caused by extreme working environments, can significantly affect the performance of NASA astronauts. Early detection and classification of the severity of stress allow for timely intervention which is crucial for improving the performance of the astronauts. However, current stress assessment approaches are largely based on questionnaires, which can be subjective. Despite the high demand for dynamic performance assessment using wearable devices, commercially available health monitors are only capable of tracking an individual’s physical activities and vital signs, failing to provide insightful information about the user’s health state at molecular levels. In this regard, sweat could serve as an excellent candidate for non-invasive stress response monitoring as it contains rich physiological information. Our 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.

Our 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. This project could provide crucial insight into the stress level of NASA astronauts and result in significant benefits by improving human performance through timely intervention.

To accomplish our goal, we propose the following objectives: (1) Develop a multimodal wearable sensor system for real-time monitoring of physical and molecular parameters. By adapting our existing physical and sweat sensing technologies, we will develop a multimodal platform for both sweat and vital sign analysis. The wearable system will wirelessly communicate with a custom designed user interface. (2) Conduct human trials on dynamic stress response assessment using the multimodal sensing system in both laboratory and real-life settings. We will conduct multiple stress-inducing training sessions to collect large sets of physical and molecular data. (3) Develop the signal processing software and determine the predictive algorithm(s) of stress and anxiety via machine learning. Sensor fusion algorithms will be used on the multimodal signals to extract the key features as well as increase the accuracy of the monitoring process. Stress and anxiety classification algorithms will analyze the derived features to detect stress levels. The combination of the multimodal system (hardware), data processing and machine learning model (software) could provide an attractive solution for accurate deep space stress assessment.

Research Impact/Earth Benefits:

Task Progress & Bibliography Information FY2020 
Task Progress: New project for FY2020.

Bibliography: Description: (Last Updated: 01/19/2023) 

Show Cumulative Bibliography
 
 None in FY 2020