Noninvasive, Wearable Multi Biosensors for Continuous, Long-term Monitoring of Blood Pressure via Internet of Things Applications. (September 2022)

• Record Type: Journal Article
• Authors: Phan, Duc Tri; Phan, Thi Tuong Vy; Huynh, Thanh Canh; Park, Sumin; Choi, Jaeyeop; Oh, Junghwan
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Wearable blood pressure -- Blood pressure response to exercise -- Pulse transit time (PTT) -- Multimodal biosensor -- Internet of thing (IoT); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108187
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Introducing a hardware and software design of a wearable device for continuous noninvasive BP monitoring using multimodal body biosensors with different physical stress tests. Different PTT landmarks and BP estimation models were presented and compared to identify the best model for blood pressure response to exercise and hypertension prediction. We assess practical applications of the proposed BP device in continuous, long-term BP monitoring in remote diagnosis and management with different exercise tasks. The feasibility of the healthcare IoT (H-IoT) application in data transmission and storage of physiological signals is proposed in the study. Abstract: Continuous blood pressure (BP) monitoring through various physical stress tests is important for the prediction of hypertension and cardiovascular mortality. This study proposes a multimodal wearable biosensor device for continuous BP monitoring through various tests during exercise and internet of things (IoT) applications. We evaluated three pulse transit time (PTT)-BP estimation models to identify the best mode for predicting BP response to exercise. Moreover, the proposed BP monitoring system was designed to connect to the cloud server for health data processing and management. The efficacy of the proposed device was validated by monitoring the natural variability of BP in various cycling tests. Thus, BP estimation based on the impedance plethysmography waveform and arterial impedance offers superiority compared to other conventional PTT estimation methods. These results suggest the feasibility of the multimodal biosensor device for continuous, long-term BP monitoring in remote diagnosis and management with IoT-based healthcare applications.