Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

Positron emission tomography (PET) imaging is an essential tool in clinical applications for the diagnosis of diseases due to its ability to acquire functional images to help differentiate between metabolic and biological activities at the molecular level. One key limiting factor in the development of efficient and accurate PET systems is the sensor technology in the PET detector. There are generally four types of sensor technologies employed: photomultiplier tubes (PMTs), avalanche photodiodes (APDs), silicon photomultipliers (SiPMs), and cadmium zinc telluride (CZT) detectors. PMTs were widely used for PET applications in the early days due to their excellent performance metrics of high gain, low noise, and fast timing. However, the fragility and bulkiness of the PMT glass tubes, high operating voltage, and sensitivity to magnetic fields ultimately limit this technology for future cost-effective and multi-modal systems. As a result, solid-state photodetectors like the APD, SiPM, and CZT detectors, and their applications for PET systems, have attracted lots of research interest, especially owing to the continual advancements in the semiconductor fabrication process. In this review, we study and discuss the operating principles, key performance parameters, and PET applications for each type of sensor technology with an emphasis on SiPM and CZT detectors—the two most promising types of sensors for future PET systems. We also present the sensor technologies used in commercially available state-of-the-art PET systems. Finally, the strengths and weaknesses of these four types of sensors are compared and the research challenges of SiPM and CZT detectors are discussed and summarized.

Due to the fast growth of the autonomous vehicles industry, there is a growing need for distance sensing systems. Among such systems, the light detection and ranging (LiDAR) system is very attractive due to their wide detection range, good spatial resolution, and high precision. For various LiDAR systems, detectors that have single photon counting (SPC) abilities are becoming increasingly popular as they have high sensitivity and can achieve long detection range. However, many publications about LiDAR only focus on the data processing and the algorithms, without sufficient considerations on the sensor-design stage. Only few studies investigated the sensor's performance in various LiDAR applications. To assist the future improvement and application of SPC LiDAR, this paper provides a new perspective by reviewing the optical sensors and the related circuits architectures in LiDAR systems, focusing on automotive applications. The principles, architectures, emerging techniques, research challenges and future directions are discussed.

With the growing importance of single-photon-counting (SPC) techniques, researchers are now designing high-performance systems based on single-photon avalanche diodes (SPADs). SPADs with high performances and low cost allow the popularity of SPC-based systems for medical and industrial applications. However, few efforts were put into the design optimization of SPADs due to limited calibrated models of the SPAD itself and its related circuits. This paper provides a perspective on improving SPAD-based system design by reviewing the development of SPAD models. First, important SPAD principles such as photon detection probability (PDP), dark count rate (DCR), afterpulsing probability (AP), and timing jitter (TJ) are discussed. Then a comprehensive discussion of various SPAD models focusing on each of the parameters is provided. Finally, important research challenges regarding the development of more advanced SPAD models are summarized, followed by the outlook for the future development of SPAD models and emerging SPAD modeling methods.

A compact high-speed active quench and reset (AQR) circuit integrated with a p ⁺ /n-well single-photon avalanche diode (SPAD) is designed and fabricated in a standard 65 nm CMOS technology. The post-layout simulations showed that the quenching time for this AQR circuit is only 0.1 ns, and the smallest dead time is 3.35 ns which corresponds a maximum count rate of ~300 Mcps. The measurements showed that the SPAD pixel achieved a dark count rate of 21 kHz, a peak photon detection probability of 23.8% at a 420 nm wavelength and a timing jitter of 139 ps (using a 405 nm pulsed laser) when the excess voltage was 0.5 V. Also, due to the short quenching time, negligible afterpulsing was observed during the measurements.

This meta-analysis and systematic review, conducted in accordance with PRISMA guidelines, explores the efficacy of cuff-less blood pressure (BP) monitoring methods, particularly focusing on photoplethysmogram-based technologies. This comprehensive analysis carefully searched prominent databases such as MEDLINE, PubMed, AMED, Embase, and IEEE-Xplore, encompassing 25 studies with a collective participant pool of 21,142 individuals. The study primarily investigates the accuracy and practicality of continuous BP estimation devices and algorithms, aiming to assess their suitability for daily or long-term, as well as their applicability and usability across a broad population. The mean disparities were 4.14 mmHg for systolic blood pressure (SBP) and 2.79 mmHg for diastolic blood pressure (DBP), highlighting a close congruence with established measurement techniques. An in-depth analysis into specific methodologies reveals that Pulse Waveform Analysis (PWA) demonstrates a more favorable performance compared to Pulse Wave Velocity (PWV) for both SBP and DBP, although these differences are not statistically significant. The findings indicate a promising future for wearable devices in short-term BP monitoring scenarios. Both PWA and PWV methods in wearable formats have shown considerable potential as effective tools for BP assessment. However, the study underscores the need for further research, particularly targeting hypertensive populations, to validate the long-term effectiveness and reliability of these wearables. Finally, this investigation is crucial for establishing the role of wearables in ongoing, reliable BP monitoring, especially when considered in conjunction with other health monitoring technologies.