Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction

-hydroxysuccinimide ester (PAA-NHS), seamlessly integrated with a liquid metal (LM) serpentine conductive path. Compared with commercial electrodes (gel-based wet electrode), this MESME exhibits high mechanical flexibility and conformability, superior breathability, robust bioadhesion, stable electrical performance under perspiration, and motion conditions and lower interfacial impedance, enabling high-fidelity multimodal signal acquisition. Leveraging this capability, the multimodal signals generated by MESME can be further integrated with deep learning for drowsiness prediction, achieving a validation accuracy of 97.14%. This platform offers a practical strategy for multimodal electrophysiological monitoring and supports future development of data-driven health assessment models.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2026-05-11
DOI
https://doi.org/10.1021/acsami.6c05481
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
0.00

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article

Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction

Dongyong Sha, Shuaimin Tang, Ao Xu, Zhuoya Wang et al.
ACS Applied Materials & Interfaces
Advanced Sensor and Energy Harvesting Materials
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Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted Physiological-State Prediction

Dongyong Sha, Shuaimin Tang, Ao Xu, Zhuoya Wang, Ding Ding, Chuanwei Zhou, Y Liu, Xinwu Yin, Yifan Ma, Yuan Yuan, Shuai Wang, Ruoxue Guo, Changsheng Liu
article en

Abstract

-hydroxysuccinimide ester (PAA-NHS), seamlessly integrated with a liquid metal (LM) serpentine conductive path. Compared with commercial electrodes (gel-based wet electrode), this MESME exhibits high mechanical flexibility and conformability, superior breathability, robust bioadhesion, stable electrical performance under perspiration, and motion conditions and lower interfacial impedance, enabling high-fidelity multimodal signal acquisition. Leveraging this capability, the multimodal signals generated by MESME can be further integrated with deep learning for drowsiness prediction, achieving a validation accuracy of 97.14%. This platform offers a practical strategy for multimodal electrophysiological monitoring and supports future development of data-driven health assessment models.

ACS Applied Materials & Interfaces
The University of Texas MD Anderson Cancer Center (US), East China University of Science and Technology (CN), Shanghai Jiao Tong University (CN), Ministry of Education (IR), Digital Science (United States) (US), Shanghai First People's Hospital (CN), Ministry of Education (TW), State Key Laboratory of Digital Medical Engineering (CN)
National Natural Science Foundation of China, Program of Shanghai Academic Research Leader
Life below water
Openalex Percentile: Top 9%
Advanced Sensor and Energy Harvesting Materials
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