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Published January 19, 2024 | in press
Journal Article Open

A physicochemical-sensing electronic skin for stress response monitoring


Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of more than 100 h with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0% and quantify psychological stress responses with a confidence level of 98.7%.

Copyright and License

© The Author(s), under exclusive licence to Springer Nature Limited 2024. 


This work was funded by the Translational Research Institute for Space Health through NASA NNX16AO69A, Office of Naval Research grant nos. N00014-21-1-2483 and N00014-21-1-2845, Army Research Office grant no. W911NF-23-1-0041, National Institutes of Health grant nos. R01HL155815 and R21DK13266, National Science Foundation grant no. 2145802, National Academy of Medicine Catalyst Award and High Impact Pilot Research Award no. T31IP1666 from the Tobacco-Related Disease Research Program and Heritage Medical Research Institute (all to W.G.). T.K.H. acknowledges the support from National Institutes of Health grant nos. T32HL144449 and T32EB027629. C.X. acknowledges support from an Amazon AI4Science Fellowship. ICP-MS instrumentation at the Resnick Sustainability Institute’s Water and Environment Lab at the California Institute of Technology was used in this work with the assistance of N. Dalleska. We acknowledge critical support and infrastructure provided for this work by the Kavli Nanoscience Institute at Caltech and Center for Transmission Electron Microscopy at the University of California Irvine, and we thank M. Hunt and M. Xu for their help.


These authors contributed equally: Changhao Xu, Yu Song, Juliane R. Sempionatto, Samuel A. Solomon.

W.G. and C.X. conceived the project. C.X. led the sensors and CARES platform development. C.X., Y.S. and J.R.S. led the platform characterization and human studies. S.A.S. and J.L. contributed to the data processing and feature extraction. H.Y.Y.N. contributed to sensor development. Y.Y., R.Y.T. and A.L. contributed to sensor characterization and testing. W.H. and J.M. contributed to wireless system development. T.K.H. and J.A.S. contributed to the human study design. W.G., C.X., Y.S., J.R.S. and S.A.S. cowrote the paper. All authors contributed to the data analysis and provided feedback on the manuscript.

Data Availability

The multimodal data collected by the CARES from human subjects is available at https://github.com/CARES-eskin/StressData. All other data that support the findings of this study are available from the corresponding author on reasonable request.

Conflict of Interest

The authors declare no competing interests.


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Additional details

January 22, 2024
January 29, 2024