Wearable Multimodal Vital Sign Monitoring Sensor With Fully Integrated Analog Front End

Abstract

Cardiovascular disease (CVD) is a widespread disease and the leading cause of death worldwide. Home care is essential for patients with CVD, and it involves the daily monitoring of important CVD-related vital signs using methods including electrocardiography (ECG), heart rate monitoring, pulse oximetry (SpO2), and continuous blood pressure measurement. However, a wearable device that can monitor these parameters simultaneously remains unavailable; herein, we propose a lightweight, highly integrated sensor that can do so. In this sensor, an analog front end (AFE) integrated chip (IC) is implemented in the sensor to detect one-lead ECG and two-wavelength photoplethysmography (PPG) signals. The highly integrated IC minimizes both the size and power requirement of the sensor. Moreover, its comprehensive functions include adjustable gain, current compensation, lead off, and fast recovery-all of which are crucial for wearable applications in large populations. Accordingly, the sensor can apply the adaptive adjust method to automatically adjust the IC parameters to suit a range of applications and users. In addition, the heart rate is calculated from the R-R interval from the ECG signals, whereas the SpO2 is calibrated with a univariate quadratic equation with less than 1% mean error. Our sensor can also calculate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) by using a support vector machine based calibration-less model with the features of infrared PPG and ECG signals. The model is trained on our pre-collected wearable dataset and has a mean error ± standard deviation of −2.10 ± 7.07 mmHg for SBP and 0.04 ± 7.34 mmHg for DBP in 16 volunteers. In conclusion, this paper reports on a multimodal, vital sign monitoring sensor-with small size, low power, and dynamic compatibility-suitable for patients with CVD under home care.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

“…The R-wave is typically used instead of the Q-Wave [ 41 ]. In many references, researchers refer to PAT simply as PTT, such as in Figure 1 d and Wang et al [ 22 ].…”

“…All of these models can complete the estimation of BP based on PTT, but there are differences in the calculation accuracy and computational complexity. In addition, PTT can also be combined with other physiological indicators (heart rate [ 44 ], age and gender [ 22 ], etc.) to form a multiple regression model, which can reflect human physiological information more comprehensively, thus improving the accuracy of continuous BP estimation.…”

“…In addition to using PTT as a single indicator, the researchers also found that the accuracy of the BP calculation model could be improved, as other static biometric indicators such as age, sex, and body mass index (BMI) were added [ 20 , 21 ]. Wang et al developed a multi-parameter BP calculation model [ 22 ] with the input of feature vectors composed of PTT, age, sex, and body mass index (BMI) as indicators and calculated the SBP and DBP with a mean error ± standard deviation of − 2.10 ± 7.07 mmHg and 0.04 ± 7.34 mmHg, and the accuracy of BP calculations has been improved to meet the requirements of the Association for the Advancement of Medical Instrumentation (AAMI). However, despite the current rapid progress in research on continuous BP monitoring devices based on PTT methods, there are still many problems because wearable monitoring sensors are susceptible to various noise sources and external disturbances [ 23 ], such as skin color, age, stress, motion artifacts, etc.…”

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System

“…The advent of wearable systems for the measuring of vital signs has started a revolution in non-laborious healthcare and sports science practices [ 1 ]. Unarguably, such systems have provided the possibility of customized continuous monitoring of physiological parameters, such as heart rate (HR), blood pressure, and respiratory rate (RR), which are of great value for both clinical and research purposes [ 2 , 3 ].…”

Development of Pressure Sensor Based Wearable Pulse Detection Device for Radial Pulse Monitoring

“…The accuracy of pulse diagnosis is highly dependent on the experiences of the doctors, and the accurate perception of pulse signals is vital for the daily monitoring of pulse and the diagnosis of individual health conditions. Currently, several types of wearable pulse detection devices have been proposed, such as volumetric pulse sensors [ 10 , 11 , 12 ] and pressure pulse sensors [ 13 , 14 , 15 ]. Volumetric pulse sensors utilize photoplethysmography (PPG) to measure the changes of blood vessel volume to characterize the pulse fluctuations.…”