Recent works on the machine learnings designed for cuffless blood pressure (BP) estimation based on measured photoplethysmogram (PPG) waveforms are reviewed by this study, with future trends of the related technology developments distilled. This review starts with those based on the conventional pulse wave velocity (PWV) theory, by which few equations are derived to calculate BPs based on measured pulse arrival times (PATs) and/or pulse transit time (PTT). Due to the inadequacy of PATs and PPTs to characterize BP, some works were reported to employ more features in PPG waveforms to achieve better accuracy. In these works, varied machine learnings were adopted, such as support vector machine (SVM), regression tree (RT), adaptive boosting (AdaBoost), and artificial neural network (ANN), etc., resulting in satisfactory accuracies based on a large number of data in the databases of Queensland and/or MIMIC II. Most recently, a few studies reported to utilize the deep learning machines like convolution neural network (CNN), recursive neural network (RNN), and long short-term memory (LSTM), etc., to handle feature extraction and establish models integrally, with the aim to cope with the inadequacy of pre-determined (hand-crafted) features to characterize BP and the difficulty of extracting pre-determined features by a designed algorithm. Therefore, the deep learning opens an opportunity of achieving much better BP accuracy by using a single PPG sensor. Favorable accuracies have been resulted by these few studies in comparison with prior works. Finally, future research efforts needed towards successful commercialization of the cuffless BP sensor are distilled.

This work is dedicated to design a novel liquid crystal (LC) lens device with multiple ring electrodes in unequal widths, in order to offer tunability on focusing quality and to lower the level of applied voltage. The number and widths of the multiple ring electrodes are pre-designed and optimized to offer the on-line tunability on individual electrode voltages to render a better refraction index distribution for focusing, as compared to the past hole-type LC lenses. The resulted refractive index distribution is expected to offer similar focusing effects based on the theory of the gradient refraction index (GRIN) lens. The transparent electrodes of this new LC lens are placed at the inner surface of the LC cell to minimize the driving voltages, in results, less than 10V, for the same level of focusing power and an easy practical operation. A new fabrication process in the wafer level to bury bus lines is developed for generating smooth electrical fields over the lens aperture. In addition, a dielectric layer is coated between electrodes and the LC layer.

A new photoplethysmography (PPG) module is designed, optimized, and fabricated in this study to obtain quality PPG signal for high-accuracy bio-sensing. The module contains four light emitting diodes (LEDs) in wavelengths of 530, 660, 850, and 940 nm and a photo-diode (PD). The distances between LEDs and PD are optimized for quality PPG signals via maximizing the ratios of pulsatile (ac) to non-pulsatile (dc) components in PPG waveforms. The optimization is carried out based on an establishment of a complete optical model that simulates well optics under the user's skin based on Beer–Lambert law. In results, the optimal LED/PD distances that lead to maximal ac/dc ratios of PPGs for different wavelengths are derived. With the optimal LEDs/PD distances in hand, a new PPG module is designed and fabricated subsequently, and further installed in a hand-held blood pressure (BP) sensing device for performance validation. Based on experimental results, for the PPG module, an ac/dc ratio of 8.02% is achieved by the proposed PPG module as opposed to 6.74% and 6.90% by two other commercial modules. As for sensing BP based on reflective pules transient time, the error is controlled well with the mean difference (MD) and standard deviation (SD) as −1.16 ± 5.38 (MD±SD) and −0.53 ± 1.92 mmHg, respectively, for systolic blood pressure and diastolic blood pressure. The resulted accuracy successfully categories the developed PPG sensor among the bests by Association for the Advancement of Medical Instrumentation and British Hypertension Society.

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This paper proposes a new pixel circuit for an active matrix organic light-emitting diode (OLED) display, which consists of five thin-film transistors (TFTs) and one capacitor. This circuit develops techniques of pulsed drive and reverse bias to achieve desired emitted brightness levels and elongate OLED life times, respectively. A current mirror is also adopted in the circuit to minimize emission nonuniformity of the OLED panel. The required input data voltages for varied displayed gray levels are calculated based on analytically known TFT and OLED models and the designed circuit architecture. The designed pixel circuit is simulated with realistic TFT models for validating expected performance to realize 256 gray levels and minimizing nonuniformity. The designed circuit is implemented in a 2.4-in quarter video graphics array panel, which shows favorable performance for minimizing display nonuniformity and alleviating OLED degradation. In addition, a closeness is clearly observed among analytical predictions, simulations, and experimental measurements.