JoWUA

Abstract

Real-time monitoring of blood pressure is crucial for detecting early hypertension and other cardiovascular diseases. However, the common method of measuring blood pressure remains invasive and intermittent, causing inconvenience and discomfort to patients. Smartwatches as a potential platform for continuous blood pressure (BP) measurements have recently attracted a great deal of interest, thanks to developments in wearables, and have been approached with different methodologies. In this paper, we propose a deep learning-based framework for predicting wearable blood pressure using smartwatch sensors. We utilize the smartwatch's optical heart rate sensor and accelerometer to collect physiological signals, including heart rate and motion. A DCNN, followed by an LSTM, is used on the signal values. The concatenated features are then fed to the regression model for estimating blood pressure from the concatenated features using a CNN-LSTM. A dataset of 50 participants' physiological signals and ground-truth blood pressures is collected to validate and test the framework. Datasets: The dataset was partitioned into training, validation, and test splits, which were used to train and test our framework. Our method achieved mean errors of 3.26 mmHg for systolic blood pressure (SBP) and 2.12 mmHg for diastolic blood pressure (DBP), which were superior to state-of-the-art results. This work enables smartwatches for non-intrusive and effortless blood pressure monitoring processes with long-term and online applications. It can be worn daily, and we use the Teal-Time blood pressure tracker to identify certain abnormal blood flow patterns. This could significantly impact the control of these conditions (such as hypertension) and likely improve overall health. For these factors, our system is generalizable to other wearables, allowing it to be widely used in the population. Our study demonstrated that blood pressure (BP) was monitored continuously under a real-world setting using a smartwatch and played a significant role in healthcare.