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Updated: May 12, 2026
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
Published on: July 22, 2022
Breathable Bioadhesive Electronic Skin for Precise Multimodal Electrophysiological Monitoring with AI-Assisted
Shuai Wang1, Xinwu Yin2, Ao Xu3
1Key Laboratory for Ultrafine Materials of Ministry of Education, Engineering Research Center for Biomedical Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai200237, P. R. China.
This study introduces a novel multimodal electrophysiological signal monitoring electrode (MESME) for wearable healthcare. The MESME ensures stable, high-fidelity signal acquisition, improving physiological monitoring and enabling accurate drowsiness prediction.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Materials Science
Background:
- Conventional electrodes lack conformal contact, breathability, and adhesion, compromising signal quality in wearable healthcare.
- Single-modality electrophysiological (EP) signals are insufficient for comprehensive health evaluation.
Purpose of the Study:
- To develop a multimodal electrophysiological signal monitoring electrode (MESME) for enhanced wearable healthcare applications.
- To improve the stability, conformability, and signal acquisition quality of skin-electrode interfaces.
Main Methods:
- Fabrication of a fiber membrane electrode combining thermoplastic polyurethane (TPU) and PAA-NHS with a liquid metal (LM) serpentine conductive path.
- Evaluation of MESME's mechanical flexibility, breathability, bioadhesion, and electrical performance under various conditions.
- Integration of multimodal signals with deep learning for drowsiness prediction.
Main Results:
- MESME demonstrated superior flexibility, conformability, breathability, and bioadhesion compared to commercial electrodes.
- Achieved stable electrical performance and lower interfacial impedance under perspiration and motion.
- Attained 97.14% validation accuracy for drowsiness prediction using deep learning with MESME data.
Conclusions:
- The developed MESME platform offers a practical strategy for high-fidelity multimodal electrophysiological monitoring.
- This technology enhances wearable healthcare by providing stable signals and enabling advanced data-driven health assessment models.