Hospitals are rightfully considered a field of indoor logistic robotics of high commercial potential. However, today, only a handful of mobile robotic solutions for hospital logistics exist that have failed to trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation, they do not easily integrate to corporate IT solutions, are not adequately shielded from cybersecurity threats, and as a result, they do not fully automate procedures and traceability of the items they carry. Moreover, existing systems are limited on scope, focusing only on delivery services, and hence do not provide any other type of support to the medical and nursing staff. ENDORSE system will address the aforementioned technical challenges and functional limitations by pursuing four innovation pillars: (i) infrastructure-less multirobot indoor navigation; (ii) advanced Human-Robot Interaction (HRI) for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions, complying with GDPR data security requirements; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring on a fleet of mobile robots, facilitating connectivity to cloud-based Electronic Health Records (EHR), and validated in an operational hospital environment for realistic assessment.

A decision support system (DSS) was developed that outputs suggestions for socket-rectification actions to the prosthetist, aiming at improving the fitness of transfemoral prosthetic socket design and reducing the time needed for the final socket design. For this purpose, the DSS employs a fuzzy-logic inference engine (IE) which combines a set of rectification rules with pressure measurements generated by sensors embedded in the socket, for deciding the rectification actions. The latter is then processed by an algorithm that receives, manipulates and modifies a 3D digital socket model as a triangle mesh formatted inside an STL file. The DSS results were validated and tested in an FEA simulation environment, by simulating and comparing the donning process among a good-fitting socket, a loose socket (poor-fit) and several rectified sockets produced by the proposed DSS. The simulation results indicate that volume reduction improves the pressure distribution over the stump. However, as the intensity of socket rectification increases, i.e., as volume reduction increases, high pressures appear in other parts of the socket which generate discomfort. Therefore, a trade-off is required between the amount of rectification and the balance of the pressure distributions experienced at the stump.

Indoor localization has gained an increase in interest recently because of the wide range of services it may provide by using data from the Internet of Things. Notwithstanding the large variety of techniques available, indoor localization methods usually show insufficient accuracy and robustness performance because of the noisy nature of the raw data used. In this paper, we investigate ways to work explicitly with range of data, i.e., interval data, instead of point data in the localization algorithms, thus providing a set-theoretic method that needs no probabilistic assumption. We will review state-of-the-art infrastructure-based localization methods that work with interval data. Then, we will show how to extend the existing infrastructure-less localization techniques to allow explicit computation with interval data. The preliminary evaluation of our new method shows that it provides smoother and more consistent localization estimates than stateof-the-art methods.

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous.

This paper presents a method that utilizes graph theory and state modelling algorithms to perform automatic complexity analysis of the architecture of cyber-physical systems (CPS). It describes cyber physical systems risk assessment (CPSRA), a tool to provide automatic decision support for enhancing the overall resilience of CPS architectures often used in critical infrastructures. CPRSA is built to enhance industrial risk assessment and improve the resilience of CPS architecture against malicious attacks on the cyber domain that can affect industrial processes, which is critical in a distributed cyber environment. Such attacks often compromise execution states on physical components and lead to hazards or even disasters through plant malfunction. CPSRA is tested against a real-world testbed model of a large SCADA system that is infused with real-world CVE vulnerabilities in some of its components. The tool creates an isomorphic graph of the CPS process model and uses graph algorithms and network analytics on the model to test cyber-attacks and evaluate attack resilience aspects. The tool’s output is then used to pinpoint high-complexity components in terms of influence on the overall CPS architecture and suggest mitigation points for security measure implementation while considering every potential subattack path and subliminal path on the model’s attack graph. The paper complements standardized assessment reports and contributes to automatic architecture assessment for critical infrastructure environments and can be used as the basis to model dependencies and threat propagation in larger digital twins, a need outlined in major NIST publications concerning the security of industrial systems that was previously done manually, without automatic insight into state and vulnerability influences.