Lactate, once merely regarded as an indicator of tissue hypoxia and muscular fatigue, has now gained prominence as a pivotal biomarker across various medical disciplines. Recent research has unveiled its critical role as a high-value prognostic marker in critical care medicine. The current practice of lactate detection involves periodic blood sampling. This approach is invasive and confined to measurements at six-hour intervals, leading to resource expenditure, time consumption, and patient discomfort. This review addresses non-invasive sensors that enable continuous monitoring of lactate in critical care patients. After the introduction, it discusses the iontophoresis system, followed by a description of the structural materials that are universally employed to create an interface between the integumentary system and the sensor. Subsequently, each method is detailed according to its physical principle, outlining its advantages, limitations, and pertinent aspects. The study concludes with a discussion and conclusions, aiming at the design of an intelligent sensor (Internet of Medical Things or IoMT) to facilitate continuous lactate monitoring and enhance the clinical decision-making support system in critical care medicine.

Urinary flow measurement and colorimetry are vital medical indicators for critically ill patients in intensive care units. However, there is a clinical need for low-cost, continuous urinary flow monitoring devices that can automatically and in real-time measure urine flow. This need led to the development of a non-invasive device that is easy to use and does not require proprietary disposables. The device operates by detecting urine flow using an infrared barrier that returns an unequivocal pattern, and it is capable of measuring the volume of liquid in real-time, storing the history with a precise date, and returning alarms to detect critical trends. The device also has the ability to detect the color of urine, allowing for extended data and detecting problems in catheterized patients such as hematuria. The device is proposed as an automated clinical decision support system that utilizes the concept of the Internet of Medical Things. It works by using a LoRa communication method with the LoRaWAN protocol to maximize the distance to access points, reducing infrastructure costs in massive deployments. The device can send data wirelessly for remote monitoring and allows for the collection of data on a dashboard in a pseudonymous way. Tests conducted on the device using a gold standard medical grade infusion pump and fluid densities within the 1.005 g/ml to 1.030 g/ml urine density range showed that droplets were satisfactorily captured in the range of flows from less than 1 ml/h to 500 ml/h, which are acceptable ranges for urinary flow. Errors ranged below 15%, when compared to the values obtained by the hospital infusion pump used as gold standard. Such values are clinically adequate to detect changes in diuresis patterns, specially at low urine output ranges, related to renal disfunction. Additionally, tests carried out with different color patterns indicate that it detects different colors of urine with a precision in detecting RGB values <5%. In conclusion, the results suggest that the device can be useful in automatically monitoring diuresis and colorimetry in real-time, which can facilitate the work of nursing and provide automatic decision-making support to intensive care physicians.

With the advancement of technology and the digitalization of manufacturing processes, optimized production of large-scale structural components faces increasing challenges due to mass product customization, workspace constraints, and the high cost of equipment such as CNC machines. Arm-based robotic manufacturing systems, as a substitutive or complementary alternative with strong potential, have gained significant attention as a means to address current challenges in machining. This work presents a state-of-the-art review of existing techniques and methods for optimizing robotic machining performance, with a particular focus on research advances in posture optimization of articulated robots. These advances are enabling the use of serial robotic arms in material removal operations as a technically and economically viable alternative to conventional machine tools. The document is structured as a comprehensive knowledge recap and systematic analysis of the technical barriers and limitations that affect dimensional quality in robotic machining—primarily due to the limited mechanical stiffness inherent in robotic arms when performing cutting tasks. It compiles and classifies a range of robot posture optimization tools. A SWOT matrix is used to identify the key technical factors that influence the suitability of optimization methods in different industrial contexts. Unlike previous reviews that address posture or dynamics in isolation, this work (i) consolidates posture optimization methods into a practical three-pillar framework (optimize–sense–compensate), (ii) maps methods to a decision-oriented SWOT for industrial selection, and (iii) extends the analysis to collaborative machining, including posture–safety trade-offs under collaborative machining standards.