Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings:A Co-Design of Sensors,Model,and DeploymentOA
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.
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
Department of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaCollege of Electronic and Information Engineering,Southwest University,Chongqing 400715,People’s Republic of ChinaDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaDepartment 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 ChinaDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaDepartment 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 ChinaDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of ChinaUnited Imaging Microelectronics Technology,Shanghai 201815,People’s Republic of ChinaDepartment of Electrical and Computer Engineering,National University of Singapore,Singapore,SingaporeDepartment of Electronic Engineering,The Chinese University of Hong Kong,Sha Tin 999077,Hong Kong SAR,People’s Republic of China
医药卫生
Wearable blood pressureResource-limitedEdgeAICardiovascular health
《Nano-Micro Letters》 2026 (5)
P.561-589,29
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