Issue

Vol. 18 (2026)

Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment

https://doi.org/10.1007/s40820-025-02003-9
Yiming Zhang
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Shirong Qiu
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Kai Du
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, People’s Republic of China
Shun Wu
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Ting Xiang
Department of Biomedical Engineering, City University of Hong Kong and Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Kenghao Zheng
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Zijun Liu
Department of Biomedical Engineering, City University of Hong Kong and Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Hanjie Chen
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Nan Ji
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China
Fa Wang
United Imaging Microelectronics Technology, Shanghai 201815, People’s Republic of China
Weijia Wu
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
Yuan‑Ting Zhang
Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin 999077, Hong Kong SAR, People’s Republic of China

Corresponding Author: Yuan‑Ting Zhang

ytzhang@cuhk.edu.hk

Nano-Micro Letters, Vol. 18 (2026), Article Number: 164

Abstract

Accurate blood pressure (BP) monitoring is essential for preventing and managing cardiovascular disease. Advancements in materials science, medicine, flexible electronic, and artificial intelligence (AI) have enabled cuffless, unobtrusive BP monitoring systems, offering an alternative to traditional sphygmomanometers. However, extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources, power efficiency, and deployment scalability. This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring, emphasizing its potential for personalized, scalable, and accessible healthcare. We systematically analyze the end-to-end system architecture, from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments. We further discuss clinical validation metrics and implementation barriers and prospective strategies. To bridge lab-to-field translation, we propose an innovative "sensor-model-deployment-assessment" co-design framework. This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.


Highlights:
1 Integrative Co-Design Framework: We synthesize current advances in sensing, models, accuracy/reliability assessment, and hardware into a sensor–model–deployment–assessment framework that organizes evidence and design trade-offs for cuffless blood pressure monitoring. The framework seeks to balance precision and efficiency by jointly considering low-power edge AI, streamlined sensor architectures, and adaptive computational models, providing a structured basis for reproducible and clinically meaningful wearable solutions.
2 Pathways to Clinical Translation: We critically assess barriers to real-world deployment, offering actionable strategies to bridge the translational gap between laboratory innovations and scalable implementation in low-resource regions with minimal healthcare infrastructure.
3 Interdisciplinary Synthesis: By integrating cutting-edge advances in materials science, digital health, and embedded AI, we provide evidence-based recommendations to empower biomedical researchers, engineers, and data scientists in advancing equitable diagnostic solutions.

Keywords

Wearable blood pressure Resource-limited EdgeAI Cardiovascular health
Zhang, Yiming, Shirong Qiu, Kai Du, Shun Wu, Ting Xiang, Kenghao Zheng, Zijun Liu, Hanjie Chen, Nan Ji, Fa Wang, Weijia Wu, and Yuan‑Ting Zhang. 2026. “Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment”. Nano-Micro Letters 18 (January):164. https://doi.org/10.1007/s40820-025-02003-9.
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