Key result
AI-integrated wearable sensors enable accurate prediction and early detection of cardiovascular abnormalities.
Why the study?
Continuous monitoring of cardiovascular health parameters is essential for early diagnosis and treatment, but integration of AI with wearable sensors for real-time monitoring requires development and evaluation.
AI-integrated wearable sensors hold promise for improving patient outcomes through real-time monitoring and early detection of cardiovascular abnormalities.
May support remote CV monitoring exploration; leaves open prospective validation before clinical adoption.
Cardiovascular diseases (CVDs) remain one of the leading causes of mortality worldwide. Continuous monitoring of cardiovascular health parameters is essential for early diagnosis, prevention, and effective treatment. Recent advancements in wearable sensor technology, artificial intelligence (AI), and wireless communication systems have revolutionized healthcare monitoring systems. This research paper explores the development of AI-integrated wearable sensors designed for real-time monitoring of cardiovascular health parameters such as heart rate, blood pressure, electrocardiogram (ECG), oxygen saturation (SpO₂), and body temperature. The study discusses the architecture, working principles, AI algorithms, sensor technologies, data analysis techniques, challenges, and future opportunities associated with wearable healthcare devices. The integration of machine learning and deep learning models enables accurate prediction and early detection of cardiovascular abnormalities. The paper highlights the importance of smart healthcare systems in improving patient outcomes, reducing hospitalization, and supporting personalized medicine.
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Bhangale et al. (2026) conducted a review in Cardiovascular diseases. AI-integrated wearable sensors was evaluated. AI-integrated wearable sensors utilizing machine learning and deep learning models enable accurate prediction and early detection of cardiovascular abnormalities.
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