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

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified with appropriate statistical measures

• Primary Outcome: Drowsiness prediction accuracy of 97.14% using multimodal electrophysiological signals

• Nnt Or Sensitivity: 97.14% accuracy for drowsiness prediction algorithm

• Confidence Interval: Not reported

Clinical Application

Technical feasibility demonstrated but clinical implementation requires further validation studies Would require TGA approval as medical device. Potential applications in sleep clinics and occupational health monitoring. Cost-effectiveness analysis needed for PBS consideration if therapeutic applications develop. Potentially applicable to patients requiring continuous electrophysiological monitoring, particularly in sleep medicine and occupational health

Abstract

Accurate and stable monitoring of electrophysiological (EP) signals is essential for wearable healthcare applications. However, conventional commercial electrodes suffer from poor conformal contact, limited breathability, and weak adhesion under sweaty skin and human motion condition, leading to unstable skin-electrode interface and low-quality signal acquisition. Moreover, single-modality EP signal measurements are insufficient for reliable physiological and health evaluation. Herein, we develop a multimodal electrophysiological signal monitoring electrode (MESME) featuring a fiber membrane combining thermoplastic polyurethane (TPU) and poly(acrylic acid) functionalized with N-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.

References

1. 1.Wang, S., Yin, X., Xu, A., Zhou, C., Ding, D., Guo, R., Wang, Z., Liu, Y., Sha, D., Tang, S., Ma, Y., Liu, C., & Yuan, Y. (2026). Breathable bioadhesive electronic skin for precise multimodal electrophysiological monitoring with AI-assisted physiological-state prediction. ACS Applied Materials & Interfaces. https://doi.org/10.1021/acsami.6c05481