Highly sensitive resistance-type flexible pressure sensor for cuffless blood-pressure monitoring by using neural network techniques

Blood pressure is an important parameter of a heart-pumping function, vascular peripheral resistance, and blood volume in human body. The non-invasive blood pressure measurement method has attracted tremendous attention due to its advantages of simple operation and comfort. In this paper, graft-modified MWCNTs and water-based polyurethane are used as filler and matrix, which are combined with simple dipping process to produce a resistance-type flexible pressure sensor with crack structure. The sensor with crack structure exhibits a high gauge factor (similar to 1582.7), fast response time (similar to 58 ms) and good repeatability (>2000 cycles) under 0-5% strain. By attaching the sensor to the radial artery of human wrist, the pulse waveform is accurately captured. By adopting the time-domain analysis method, in addition to the fast Fourier transform and threshold method which is employed to position and extract the characteristic value of pulse signal, a three-layer back propagation neural network is constructed to regress blood pressure value. The correlation coefficients of systolic blood pressure, diastolic blood pressure and mean blood pressure were 0.950, 0.875 and 0.947, respectively, which could meet the A-level standards of British Hypertension Society and Association for the Advancement of Medical Instrumentation. This study provides a good real-time and portable technology for home blood pressure measurement.