Key Takeaways & Executive Findings
- •• Integrative Co-Design Framework: Synthesizes advances in sensing, models, accuracy/reliability assessment, and hardware into a sensor–model–deployment–assessment framework, balancing precision and efficiency for cuffless BP monitoring. • Pathways to Clinical Translation: Critically assesses barriers to real-world deployment, offering actionable strategies to bridge the translational gap for scalable implementation in low-resource regions. • Interdisciplinary Synthesis: Integrates materials science, digital health, and embedded AI to provide evidence-based recommendations for equitable diagnostic solutions. • Global Health Equity: Emphasizes potential of AI-enhanced wearable BP monitoring to support proactive hypertension control and promote cardiovascular health equity worldwide.
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.
1. Introduction
Hypertension is a major risk factor for cardiovascular diseases (CVDs), contributing significantly to global morbidity and mortality [1]. Accurate and continuous blood pressure (BP) monitoring is thus essential for early diagnosis, preventive care, and personalized intervention [2], particularly in resource-limited settings where access to episodic clinical measurement is limited. Traditional cuff-based BP measurement [3], despite its clinical acceptance, remains inherently episodic, cumbersome, and ill-suited for unobtrusive long-term monitoring [4]. Cuffless BP estimation represents a paradigm shift in non-invasive monitoring by eliminating the need for traditional cuffs, supporting cost-effective, continuous BP monitoring during daily life and holds potentials for personalized, proactive hypertension management [5].
Recent advances in sensing technologies have further empowered this field, enabling the acquisition of high-quality physiological data through increasingly miniaturized and affordable wearable devices [6–9]. Concurrently, artificial intelligence (AI) has emerged as a transformative tool for analyzing these complex signals, significantly enhancing the accuracy and robustness of cuffless BP estimation [8]. These trends have created new opportunities for deploying AI-driven BP monitoring beyond traditional healthcare settings. In particular, resource-limited settings—including low- and middle-income countries (LMICs), remote communities, and underserved populations in high-income countries—represent environments where the potential impact of wearable BP monitoring is especially high [6, 10, 11]. These settings are often characterized by limited healthcare infrastructure, insufficient access to trained personnel, and high unmet needs for hypertension screening and management. Yet, deploying state-of-the-art systems in such contexts remains challenging due to constraints in computational resources, power efficiency, and deployment scalability.
Loading authentic research manuscript (Pages 1–5)...
Yiming Zhang, Shirong Qiu, Kai Du, Shun Wu, Ting Xiang, Kenghao Zheng, Zijun Liu, Hanjie Chen, Nan Ji, Fa Wang, Weijia Wu, 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. https://doi.org/10.1007/s40820-025-02003-9
Research & Educational Purpose Only:The translations, structured abstracts, analytical annotations, and data reports provided by SinoTechIntel are intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoTechIntel claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the main focus of this review?
The review focuses on AI-enhanced wearable blood pressure monitoring, particularly its application in resource-limited settings. It synthesizes advances in sensing, AI models, and deployment strategies, and proposes a co-design framework to bridge the gap between laboratory innovations and real-world implementation.
What are the key challenges for deploying wearable BP monitoring in low-resource settings?
Key challenges include limited computational resources, power efficiency constraints, and scalability issues. Additionally, there is a lack of healthcare infrastructure and trained personnel, which hinders the translation of lab-based innovations to practical use.
What is the proposed co-design framework?
The proposed framework is a 'sensor-model-deployment-assessment' co-design approach. It integrates sensor design, AI model development, deployment strategies, and clinical validation to ensure that wearable BP monitoring systems are accurate, efficient, and suitable for resource-limited environments.
How can AI enhance wearable blood pressure monitoring?
AI enhances wearable BP monitoring by improving the accuracy and robustness of cuffless BP estimation through advanced signal processing and machine learning models. It enables personalized and adaptive algorithms that can operate efficiently on edge devices, making continuous monitoring feasible in everyday settings.
What is the significance of this review for global health?
This review highlights the potential of AI-enhanced wearable BP monitoring to improve hypertension management and cardiovascular health equity, especially in underserved populations. By addressing deployment challenges, it provides a roadmap for scalable and accessible healthcare solutions worldwide.
Related Technical Papers & Translations
Direct Repair of the Crystal Structure and Coating Surface of Spent LiFePO4 Materials Enables Superfast Li-Ion Migration
The rapid accumulation of spent LiFePO4 (LFP) cathodes from retired lithium-ion batteries necessitates the development of effective and environmental-friendly recycling strategies. In this context, direct regeneration has emerged as a promising approach for reclaiming LFP cathode materials, offering a streamlined pathway to restore their electrochemical functionality. We report an integrated regeneration protocol that simultaneously repairs the degraded crystal structure and reconstructs the damaged carbon coating in spent LFP. The regenerated cathode material had superfast lithium-ion diffusion kinetics and a stable cathode–electrolyte interface, giving a remarkable rate capability with specific capacities of 122 mAh g−1 at 5C and 106 mAh g−1 at 10C (1C = 170 mA g−1). It also maintained capacities of 110.7 mAh g−1 (5C) and 84.1 mAh g−1 (10C) after 400 cycles. It could be used in harsh environments and could be stably cycled at subzero temperatures (−10 and −20 °C) and in solid-state electrolyte batteries. Life cycle assessment combined with economic evaluation using the EverBatt model reveals that this direct regeneration approach has high economic and environmental benefits.
Oxide Semiconductor for Advanced Memory Architectures: Atomic Layer Deposition, Key Requirement and Challenges
Oxide semiconductors (OSs), introduced by the Hosono group in the early 2000s, have evolved from display backplane materials to promising candidates for advanced memory and logic devices. The exceptionally low leakage current of OSs and compatibility with three-dimensional (3D) architectures have recently sparked renewed interest in their use in semiconductor applications. This review begins by exploring the unique material properties of OSs, which fundamentally originate from their distinct electronic band structure. Subsequently, we focus on atomic layer deposition (ALD), a core technique for growing excellent OS films, covering both basic and advanced processes compatible with 3D scaling. The basic surface reaction mechanisms—adsorption and reaction—and their roles in film growth are introduced. Furthermore, material design strategies, such as cation selection, crystallinity control, anion doping, and heterostructure engineering, are discussed. We also highlight challenges in memory applications, including contact resistance, hydrogen instability, and lack of p-type materials, and discuss the feasibility of ALD-grown OSs as potential solutions. Lastly, we provide an outlook on the role of ALD-grown OSs in memory technologies. This review bridges material fundamentals and device-level requirements, offering a comprehensive perspective on the potential of ALD-driven OSs for next-generation semiconductor memory devices.
Laser powder bed fusion of biodegradable Zn-4Cu alloy: Processing, microstructure and properties
Zn's natural degradability and biocompatibility make it a promising candidate for implants, however, its mechanical properties remain insufficient for bone applications. In this study, the performance of Zn was enhanced by developing Zn-Cu alloys via laser powder bed fusion (LPBF). Optimal LPBF parameters for forming stable tracks were achieved by adjusting laser power and scanning speed. Under optimized conditions of 100 W and 100 mm/s, high-density (99.58%) Zn-Cu alloys with improved hardness (68.2HV) and yield strength (160 MPa) were achieved. These improvements are attributed to solid solution strengthening, segregation strengthening, and grain refinement. The Zn-Cu alloys also demonstrated favorable degradation behavior, with a rate of 0.16 mm/year. This degradation is primarily driven by micro-galvanic corrosion between the CuZn5 phase and Zn matrix, along with refined grains and increased grain boundary density. This work demonstrates a viable strategy for fabricating Zn-based implants with enhanced structural integrity and mechanical performance via LPBF.