Biomedical sensors are the key units of medical and healthcare systems. The development focus of this topic is to use new technology and advanced functional biocompatible materials to design miniature, intelligent, reliable, multifunctional, low-cost, and efficient sensors. The last two decades have seen unprecedented growth in the employment of advanced sensors, which enable the detection of critical biomarkers for the early diagnosis of human diseases and the monitoring of human physiological signals for assessments in healthcare and biomedical applications. This rapid progress in both sensor technology development and its applications is mainly due to the quickly advancing development of micro/nanofabrication, manufacturing techniques, and advanced materials, as well as the increasing demand for the development of fast, simple, and sensitive measurement techniques that are capable of accurately and reliably monitoring biological samples in real time. The development of biomedical sensors is driven by the requirements of the medical field. The screening and continuous monitoring of patients with sensors has become increasingly important. A huge growth in the demand for home care will certainly promote the development of disposable sensors or telemedicine. This also puts forward requirements for future medical sensors.

This Special Issue aims to provide an overview of recent advancements in the area of sensing technologies, including of sensors and platforms with a focus on functional materials, novel sensing mechanisms, design principles, fabrication and characterization techniques, performance optimization methods, multifunctional and multiplex sensing platforms, and system integration strategies, which play crucial roles in many applications.

Gökhan Güney et al.

[1] used MediaPipe artificial intelligence (AI)-based handtracking technologies to quantitatively assess the hand movements of patients that were suffering from Parkinson's Disease (PD). First, they investigated the frequency and amplitude relationship between the video and accelerometer data. Then, they focused on quantifying the effects of taking standard oral treatments. Their work achieved an automatic estimation of the movement frequency and tremor frequency with a low error rate, and this appears to be the first paper that has presented an automated tremor analysis before/after the use of medication for PD, and, in particular, the first to use high-frame-rate video data. Athanasios Tsanas et al. [2] proposed a new acceleration summary measure, the Rate of Change Acceleration Movement (ROCAM), and compared its performance against three established approaches, summarizing the three-dimensional acceleration data to replicate the minute-by-minute labels. Moreover, they compared findings where the acceleration data was sampled at 10, 25, 50, and 100 Hz. Collectively, this study contributed new insights into the analysis of wrist-worn actigraphy data in three areas, and provided insights into