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Uroflowmetry (UF) is a crucial guideline-recommended tool for men with benign prostatic obstruction (BPO). Moreover, UF is a helpful decision-making tool for the management of patients with lower urinary tract symptoms (LUTS) and benign prostatic hyperplasia (BPH). In the last few years, telemedicine and telehealth have increased exponentially as cost-effective treatment options for both patients and physicians. Telemedicine and telehealth have been well positioned during the COVID-19 pandemic to prevent healthcare system overload and to ensure adequate management of patients through screening, diagnosis, and follow-up at home. In the present manuscript, the main characteristics and performance of a novel and low-cost device for home-based UF have been analyzed. The simple weight-transducer method has been applied to perform UF. An inexpensive load cell connected to a 24 bit analogic digital converter (ADC) sends data to a cloud server via SIM card or home Wi-Fi. Data are processed and shown in graphics with both volume and flow rate as a function of time, allowing for measurement of average flow rate, maximum flow rate, voided volume, and voiding time. A numerical algorithm allows for filtering of the dynamic effect due to the urine gravity acceleration and for removing the funnel to simplify the home measurement procedure. Through an online platform, the physician can see and compare each UF data. The device’s reliability has been validated in a first laboratory setting and showed excellent performance. This approach based on domiciliary tests and an online platform can revolutionize the urologic clinic landscape by offering a constant patient cost-effective follow-up, eliminating the time wasted waiting in the office setting.

A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole–Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren–Lawrence grade ranging in [1,4]), with an average error of 3%.

In this work, an augmented reality (AR) system is proposed to monitor in real time the patient’s vital parameters during surgical procedures. This system is characterised metrologically in terms of transmission error rates and latency. These specifications are relevant for ensuring real-time response. The proposed system automatically collects data from the equipment in the operating room (OR), and displays them in AR. The system was designed, implemented and validated through experimental tests carried out using a set of Epson Moverio BT-350 AR glasses to monitor the output of a respiratory ventilator and a patient monitor in the OR.

A customizable Finite Elements Model of human knee is proposed for improving inter-individual reproducibility in NSAIDs transdermal delivery measurement. The model simulates: (i) the measurement system, based on Bio-Impedance Spectroscopy, and (ii) the system under test, namely the knee by five parallel, homogeneous, and concentric layers: bone, muscle, adipose tissue, wet skin, and dry skin. In this paper, first the equations and the architecture of the model are described. Then, the results of the numerical characterization and the preliminary experimental validation are reported. A sensitivity analysis was realized for reducing computational burden during Model customization. Only five parameters out of the 64 used in the Cole-Cole equation were sufficient for fitting experimental data of different subjects.