Integrating Artificial Intelligence in Wearable Technologies for Blood Pressure Monitoring in Body Area Networks

Background and Aims: Wearable technologies in Body Area Networks (BAN) have emerged as innovative tools for health monitoring, particularly in continuous blood pressure measurement. Artificial Intelligence (AI), with its ability to process complex data and learn dynamic patterns, plays a pivotal role in enhancing the accuracy, efficiency, and personalization of these systems. This article explores the applications of AI in wearable blood pressure monitoring technologies within the BAN framework. Methods: This study conducted a systematic review of international peer-reviewed articles (۲۰۱۵–۲۰۲۳) to analyze the applications of AI in processing physiological signals (such as PPG and ECG), noise reduction, blood pressure prediction, and multi-sensor data integration. Machine Learning (ML) and Deep Learning (DL) algorithms, including SVM, Random Forest, LSTM, and CNN, were evaluated. Results: The findings demonstrated that AI significantly improves wearable blood pressure monitoring through high-accuracy signal processing (e.g., noise removal and feature extraction), precise blood pressure prediction (DL models using PPG data showed errors below ۵ mmHg compared to standard methods), personalized user adaptation via adaptive learning, and optimized sensor integration in BANs for energy efficiency. Deep Learning algorithms, particularly CNN and LSTM, outperformed classical ML methods (e.g., SVM and Random Forest) in estimating blood pressure (MAE ۵ mmHg), owing to their ability to autonomously extract complex features from PPG and ECG data. Conclusion: The integration of AI with wearable technologies in BANs has revolutionized blood pressure monitoring. Despite technical challenges, AI holds undeniable potential for transforming wearables into precise, automated monitoring tools. Future research should focus on clinical validation, expanding data diversity, and reducing computational costs.

کلیدواژه ها:

Artificial Intelligence ، Wearable Blood Pressure Monitoring