Wearable Biosensors for Remote Monitoring and Early Detection of Cardiovascular Diseases: A Systematic Literature Review

Abstract

Purpose: The current state of wearable biosensors for remote monitoring and early cardiovascular disease (CVD) detection is examined in this systematic literature review, which also highlights the technological advancements, clinical applicability, and implementation difficulties of these devices.
Methodology: A thorough search for peer-reviewed research published between 2020 and 2025 was carried out using PRISMA guidelines across Scopus and Web of Science. A total of thirty studies met the inclusion criteria. The PROBAST tool was used to evaluate the risk of bias in the data that was extracted on device type, biosensors, monitored parameters, clinical settings, and methodological quality.
Results: Wearable biosensors have shown considerable promise in monitoring metrics like blood pressure, heart rate, and electrocardiogram. Diagnostic accuracy has increased due to integration with machine learning algorithms; some models have over 90% accuracy in identifying conditions like hypertension and atrial fibrillation. However, only a small percentage of studies included clinical validation; most were restricted to simulations or prototype evaluations. Motion artefacts, data security, power constraints, and a lack of standardisation are some of the main obstacles.
Novelty and Contribution: This review provides a current evaluation of wearable CVD monitoring technologies by synthesising recent developments in biosensor miniaturisation, multi-modal sensing, and AI-driven analytics. It shows that hospital-based diagnostics are gradually giving way to ongoing, individualised health monitoring.
Implications: Wearable biosensors have considerable potential to improve early intervention, lessen healthcare costs, and facilitate remote care models. Additional clinical validation, regulatory alignment, and strong integration into healthcare infrastructures are necessary to realise this potential.