Paper-Based Supercapacitive Pressure Sensor for Wrist Arterial Pulse Waveform Monitoring

Wearable System Integrating Dual Piezoresistive and Photoplethysmography Sensors for Simultaneous Pulse Wave Monitoring

Wearable, flexible piezoresistive pressure sensors have garnered substantial interest due to their diverse applications in fields such as electronic skin, robotic limbs, and cardiovascular monitoring. Among these applications, arterial full pulse waveform monitoring stands out as a critical area of research. The emergence of piezoresistive pressure sensors as a prominent tool for capturing pulse waveforms has led to extensive investigations. However, the lack of a linear response while achieving high sensitivity, limited compactness of signal collection systems, and scaling issues are a few potential causes of the gap between research advances and technology market readiness. Here, we present a scalable dual piezoresistive sensor that uses two complementary resistance-changing mechanisms to balance the trade-off between linear response and high sensitivity. This synergic design enables excellent sensitivity of 8.4 kPa-1 and near linear pressure response. It boasts a fast response time of 95/145 ms for loading and unloading at a pressure of 10.1 kPa. The durability tests, encompassing nearly 5000 two-stage compression cycles, validate the reliable performance of the sensor, even after prolonged use. Leveraging these performance metrics, we developed a high-resolution and compact signal collection device capable of accurately detecting the three distinct peaks associated with the full pulse waveform, including the systolic and reflected diastolic peaks. Moreover, the signal acquisition system incorporates a photoplethysmography sensor, which, when paired with the dual piezoresistive sensor, offers new insights into localized vascular health monitoring. The unique feature of our approach is its ability to perform localized vascular monitoring using multiple sensors placed on different veins. The simultaneous detection of forward- and backward-going pulse waves at the body extremities allows for a comprehensive evaluation of localized vascular health, which is not achievable with a single sensor. Finally, the comparison of wrist pulse waveforms between the compact signal collection device and a standard sourcemeter (such as the Keithley 2460) confirmed that the wearable sensing system is on par with conventional sourcemeters in terms of capturing the typical details of the full pulse waveform (i.e., systolic peak, dicrotic notch, and diastolic peak). This validation underscores the reliability and effectiveness of the developed wearable sensing system for practical and continuous pulse waveform monitoring.

High-Performance Supercapacitive Pressure Sensors via Height-Grading Micro-Domes of Ionic Conductive Elastomer

Soft capacitive sensors present numerous appealing characteristics, including simple structure, low power consumption, and fast response. However, they often suffer from low sensitivity and a limited linear sensing range. Herein, a concept is presented to enhance the sensitivity and linearity of supercapacitive pressure sensors by functionally grading the heights of macrodomes constructed from a highly elastic and ionic conductive elastomer made of poly(vinyl alcohol) and phosphoric acid (PVA/H3PO4). The resultant supercapacitive sensors exhibit a high sensitivity (423.42 kPa-1), wide linear sensing range (0-400 kPa), ultralow limit of detection (0.48 Pa), and high durability (stable signal outputs up to 5000 cycles of loading/unloading). Additionally, the sensors can maintain consistent sensing performance within a temperature range of 25-40 °C. The potential of the sensor in health monitoring is demonstrated through ultrahigh-resolution weight measurement, pulse detection, and respiration monitoring.

Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning

Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.