Key result
Wearables paired with deep learning may enable continuous BP and PWV estimation for personalized medicine.
Why the study?
Continuous monitoring of BP and pulse wave velocity via wearable devices has emerged as a potential alternative for healthcare, prompting a review of estimation techniques and implementation challenges.
Wearable devices utilizing PPG and ECG signals combined with advanced deep learning algorithms offer a promising, non-invasive approach for continuous blood pressure and arterial stiffness monitoring to enhance personalized cardiovascular care.
Continuous monitoring of personal physiological parameters, such as blood pressure (BP) and pulse wave velocity, through wearable devices has emerged as a potential alternative for healthcare. This review discusses the variety of wearable devices and signal properties of different measurement sites. Recent advancements in estimation techniques applied to wearable devices for cardiovascular health monitoring are revisited. Concerns including data leak and validation criterion are highlighted. Photoplethysmography morphology and BP circadian variability are also addressed. Accordingly, the approaches are categorised and analysed based on the study protocols. The potential opportunities due to the development of deep learning algorithms are examined for BP and pulse wave velocity estimations. As the evolution of wearable devices progresses, multidisciplinary collaboration becomes crucial and necessary for realising personalised smart medicine.
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Chang et al. (2025) conducted a review in Cardiovascular health monitoring (blood pressure and pulse wave velocity). Wearable devices was evaluated. Wearable devices combined with deep learning algorithms offer potential opportunities for continuous estimation of blood pressure and pulse wave velocity for personalized smart medicine.
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