Diabetes has become a chronic metabolic disorder, and the growing diabetes population makes medical care more important. We investigated using a portable and noninvasive contact lens as an ideal sensor for diabetes patients whose tear fluid contains glucose. The key feature is the reversible covalent interaction between boronic acid and glucose, which can provide a noninvasive glucose sensor for diabetes patients. We present a phenylboronic acid (PBA)-based HEMA contact lens that exhibits a reversible swelling/shrinking effect to change its thickness. The difference in thickness can be detected in a picture taken with a smartphone and analyzed using software. Our novel technique offers the following capabilities: (i) non-enzymatic and continuous glucose detection with the contact lens; (ii) no need for an embedded circuit and power source for the glucose sensor; and (iii) the use of a smartphone to detect the change in thickness of the contact lens with no need for additional photo-sensors. This technique is promising for a noninvasive measurement of the glucose level and simple implementation of glucose sensing with a smartphone.

Non-invasive continuous blood pressure measurement is an emerging issue that potentially can be applied to cardiovascular disease monitoring and prediction. Recently, many groups have proposed the pulse transition time (PTT) method to estimate blood pressure for long-term monitoring. However, the PTT-based methods for blood pressure estimation are limited by non-specific estimation models and require multiple calibrations. This study aims to develop a low-cost wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement. The pressure change in the radial artery was extracted by systolic and diastolic feature points in pressure pulse wave (PPW) and the pressure sensitivity of the sensor. The proposed system showed a reliable accuracy of systolic blood pressure (SBP) (mean absolute error (MAE) ± standard deviation (SD) 1.52 ± 0.30 mmHg) and diastolic blood pressure (DBP, MAE ± SD 1.83 ± 0.50), and its performance agreed with standard criteria of MAE within 5 mmHg and SD within ±8 mmHg. In conclusion, this study successfully developed a low-cost, high-accuracy piezoelectric-based system for continuous beat-to-beat SBP and DBP measurement without multiple calibrations and complex regression analysis. The system is potentially suitable for continuous, long-term blood pressure-monitoring applications.

The aim of this study was to establish a rapid prescreening tool for heart failure with preserved ejection fraction (HFpEF) by using artificial intelligence (AI) techniques to detect abnormal echocardiographic patterns in structure and function on the basis of intrabeat dynamic changes in the left ventricle and the left atrium. Although diagnostic criteria for HFpEF have been established, rapid and accurate assessment of HFpEF using echocardiography remains challenging and highly desirable. In total, 1,041 patients with HFpEF and 1,263 asymptomatic individuals were included in the study. The participants' 4-chamber view images were extracted from the echocardiographic files and randomly separated into training, validation, and internal testing data sets. An external testing data set comprising 150 patients with symptomatic chronic obstructive pulmonary disease and 315 patients with HFpEF from another hospital was used for further model validation. The intrabeat dynamics of the geometric measures were extracted frame by frame from the image sequence to train the AI models. The accuracy, sensitivity, and specificity of the best AI model for detecting HFpEF were 0.91, 0.96, and 0.85, respectively. The model was further validated using an external testing data set, and the accuracy, sensitivity, and specificity became 0.85, 0.79, and 0.89, respectively. The area under the receiver-operating characteristic curve was used to evaluate model classification ability. The highest area under the curve in the internal testing data set and external testing data set was 0.95. The AI system developed in this study, incorporating the novel concept of intrabeat dynamics, is a rapid, time-saving, and accurate prescreening method to facilitate HFpEF diagnosis. In addition to the classification of diagnostic outcomes, such an approach can automatically generate valuable quantitative metrics to assist clinicians in the diagnosis of HFpEF.

Non-contact electrocardiogram (ECG) measurement is an advanced sensing technique that uses capacitive electrodes to detect cardiac signals through non-conductive fabrics. However, the capacitive coupling is a significant factor that affects signal-to-noise ratio (SNR) of non-contact ECG, including skin-electrode active area, material, and thickness of the non-conductive fabric. This study aims to develop a high-fidelity non-contact ECG system to evaluate the influence of capacitive coupling on ECG measurement. In this study, a polymer foam with low surface resistance (0.05Ω/inch ² ) was designed for improving the capacitive-coupling interface between the curved body and electrode sensing surface. The system recorded excellent non-contact ECG of 29.8dB, and the accuracy of heart rate was 99.5% compared to wet-contact ECG measurement. The SNR exponentially attenuated with decreasing skin-electrode capacitance by the combined evidence of theoretical calculation and experimental results. The proposed system generates distinguishable ECG signals (SNR>0dB) at the skin-electrode capacitance above 85pF and maximum through-thickness of cotton-based cloth of 1.2mm. In conclusion, this study evaluated the influence of capacitive coupling on non-contact ECG measurements and established a lower bound of the coupling capacitance for satisfactory signal quality. Future studies may investigate whether the coupling capacitance can be further reduced.

Pulse transit time (PTT) sensing technology is rapidly evolving for cuffless continuous blood pressure (BP) monitoring. However, PTT-based BP methods must implement multidevices that could impede wearable applications. This work presents the study and development of a single-channel wrist-worn impedance plethysmography (IPG) system for wearable continuous BP measurement. IPG-based BP mathematical model with two-step calibration was derived for single physiological signal acquisition. The high-resolution IPG measurement was performed by optimized injection current frequency and electrode arrangement in response to obvious impedance difference between systolic BP (SBP) and diastolic BP (DBP) feature points in the IPG waveform, resulting in an impedance difference of 594 $\text{m}\Omega $ . The BP accuracy results within 30 subjects indicated that SBP estimation error was 2.01 ± 1.40 mmHg and 1.83 ± 1.29% in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE), and 2.26 ± 1.43 mmHg and 4.96 ± 3.02% for DBP estimation. The novel system could potentially be implemented as a wearable cardiovascular monitoring device with two-in-one functions (continuous BP and heart rate monitoring) using a single physiological sensor in the future.