Cardiovascular disease is the leading cause of death worldwide and coronary artery disease is its most prevalent manifestation, associated with high mortality and morbidity. In clinical practice cardiac troponins (cTn) are the cornerstone of the diagnosis, risk stratification and thus selection of the optimal treatment strategy in patients with acute coronary syndrome. According to the third update of the universal definition of myocardial infarction (MI) cTn is the preferred cardiac biomarker of myocardial necrosis in the setting of acute myocardial ischemia. Over the last years newer high sensitivity cardiac troponin (hs-cTn) assays have been developed that are more sensitive than conventional assays, have low limit of detection, low imprecision and low reference limits, but due to variability, the deployment of a standardization and harmonization method is required before their wide use in clinical practice. Recent studies have shown that their utilization seems to improve the diagnostic accuracy detecting MI in patients presenting with chest pain. However, the improved sensitivity comes along with a decreased specificity, though serial cTn measurements and the detection of early changes could improve the specificity and the overall diagnostic performance. Moreover, apart from their use in the diagnosis and risk stratification of MI and acute coronary syndromes, hs-cTn assays seem to have a key role in risk stratification and short and long-term prognosis in a variety of cardiovascular modalities such as stable coronary disease, heart failure and acute pulmonary embolism. In addition, studies have suggested that cTns may be used as a biomarker in the primary prevention of cardiovascular disease leading to the identification of high-risk populations or individuals with silent heart disease.

The automation of manufacturing applications where humans and robots operate in a shared environment imposes new challenges for presenting the operator’s safety and robot’s efficiency. Common solutions relying on isolating the robots’ workspace from human access during their operation are not applicable for HRI. This paper presents an extended reality-based method to enhance human cognitive awareness of the potential risk due to dynamic robot behavior towards safe human–robot collaborative manufacturing operations. A dynamic and state-aware occupancy probability map indicating the forthcoming risk of human–robot accidental collision in the 3D workspace of the robot is introduced. It is determined using octrees and is rendered in a virtual or augmented environment using Unity 3D. A combined framework allows the generation of both static zones (taking into consideration the entire configuration space of the robot) and dynamic zones (generated in real time by fetching the occupancy data corresponding to the robot’s current configuration), which can be utilized for short-term collision risk prediction. This method is then applied in a virtual environment of the workspace of an industrial robotic arm, and we also include the necessary technical adjustments for the method to be applied in an AR setting.

Significant advancements have been made towards the automation of tasks involving highly deformable object manipulation. However, due to its complexity, predicting the behaviour of such objects, tasks requiring precision and flexibility remain far from full automation. In this paper, a method for the generation of a digital twin is proposed to serve as a solution for robotized cloth manipulation tasks. To estimate mechanical parameters and reconstruct a cloth’s digital twin, our approach integrates image processing and a genetic algorithm process. Specifically, a spatio-temporal graph of the cloth is built using Scale-Invariant Feature Transform (SIFT), and a genetic algorithm is used to optimize the parameters of a mass-spring-damper (MSD) model. Contrary to prior methods, which were relying on predefined cloth models or computationally expensive simulations (e.g., FEM), the proposed framework enables lightweight, data-driven parameter tuning from a single monocular RGB video showing the deformations of the cloth during a set of predefined movements by a pair of robotic arms. The generated digital twin is then used to calculate the optimal trajectory for the robotic arms in typical cloth manipulation tasks and is subsequently evaluated in real-world experiments. Tasks such as laying a cloth on a table and folding it in half are used to validate the method’s accuracy and applicability in both simulated and physical environments. The proposed method has potential applications in industrial textile handling, domestic service robotics, and elderly care, particularly in settings where low-cost, sensor-efficient automation is desirable.