Issue

Vol. 18 (2026)

Modularly-Assembled Smart Microneedle Platform for Machine Learning-Driven Personalized Health Monitoring

https://doi.org/10.1007/s40820-026-02095-x
Hongyi Sun
School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, People’s Republic of China
Lechen Chen
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
Tao Wang
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
Zhuoheng Li
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
Yi Shi
School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, People’s Republic of China
Wen Lv
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
Zhi Yang
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
Fuzhen Xuan
Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
Min Zhang
School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, People’s Republic of China
Guoyue Shi
School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, People’s Republic of China

Corresponding Author: Guoyue Shi

gyshi@chem.ecnu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 248

Abstract

Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and customized approaches to continuously monitor a broad spectrum of physiologically relevant biomarkers for personalized health management. Moreover, existing data-based analytical strategies remain inadequate for delivering quantitative and predictive evaluations of health status in real-life settings. Here, we report an electronic multiplexed microneedle-based biosensor patch (eMPatch) that enables real-time, minimally invasive monitoring of key metabolic biomarkers in interstitial fluid, including glucose, uric acid, cholesterol, sodium, potassium, and pH. By integrating modular microneedle (MN) sensors into a skin-interfaced flexible platform, the eMPatch achieves robust mechanical stability and seamless skin conformity, thereby ensuring reliable and continuous sensing within the dermal space. In vivo validation in animal models under metabolic intervention highlights the strong capability of the eMPatch for real-time physiological tracking across diverse daily activities. Implemented with a machine learning algorithm, the eMPatch enables automatic feature extraction and multi-task health assessment, achieving a classification accuracy of 0.996 in distinguishing normal and diet-induced metabolic disorder for health condition identification and an R2 score of 0.977 for the corresponding degree evaluation. This study highlights the potential of the MN-integrated, machine learning-enhanced biosensing platform toward personalized health management.


Highlights:
1 A skin-interfaced microneedle patch simultaneously and continuously measures six metabolic biomarkers from dermal interstitial fluid—glucose, uric acid, cholesterol, sodium, potassium, and pH.
2 Modular microneedle units assembled on a compliant polystyrene-isoprene-polystyrene substrate offer mechanical robustness and excellent flexibility, enabling seamless adhesion, stable skin-sensor coupling, and user-specific configuration, which delivers durable, conformal wear with high signal fidelity in daily use.
3 An end-to-end personalized health evaluation system: high-dimensional multiplexed signals are processed by an optimized machine-learning pipeline to quantify and predict metabolic responses to daily behaviors, supporting personalized guidance (e.g., postprandial control, electrolyte balance).

Keywords

Microneedle Multiplexed sensing Flexible patch Machine learning Personalized health
Sun, Hongyi, Lechen Chen, Tao Wang, Zhuoheng Li, Yi Shi, Wen Lv, Zhi Yang, Fuzhen Xuan, Min Zhang, and Guoyue Shi. 2026. “Modularly-Assembled Smart Microneedle Platform for Machine Learning-Driven Personalized Health Monitoring”. Nano-Micro Letters 18 (February):248. https://doi.org/10.1007/s40820-026-02095-x.
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