The knee is one of the most stressed joints of the human body, being susceptible to ligament injuries and degenerative diseases. Due to the rising incidence of knee pathologies, the number of knee X-rays acquired is also increasing. Such X-rays are obtained for the diagnosis of knee injuries, the evaluation of the knee before and after surgery, and the monitoring of the knee joint’s stability. These types of diagnosis and monitoring of the knee usually involve radiography under physical stress. This widely used medical tool provides a more objective analysis of the measurement of the knee laxity than a physical examination does, involving knee stress tests, such as valgus, varus, and Lachman. Despite being an improvement to physical examination regarding the physician’s bias, stress radiography is still performed manually in a lot of healthcare facilities. To avoid exposing the physician to radiation and to decrease the number of X-ray images rejected due to inadequate positioning of the patient or the presence of artefacts, positioning systems for stress radiography of the knee have been developed. This review analyses knee positioning systems for X-ray environment, concluding that they have improved the objectivity and reproducibility during stress radiographs, but have failed to either be radiolucent or versatile with a simple ergonomic set-up.

Frequently carrying high loads and performing repetitive tasks compromises the ergonomics of individuals, a recurrent scenario in hospital environments. In this paper, we design a logistic planner of a fleet of autonomous mobile robots for the automation of transporting trolleys around the hospital, which is independent of the space configuration, and robust to loss of network and deadlocks. Our robotic solution has an innovative gripping system capable of grasping and pulling nonmodified standard trolleys just by coupling a plate. Robots are able to navigate autonomously, to avoid obstacles assuring the safety of operators, to identify and dock a trolley, to access charging stations and elevators, and to communicate with the latter. An interface was built allowing users to command the robots through a web server. It is shown how the proposed methodology behaves in experiments conducted at the

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Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists.

We propose a new approach, namely Optimized Embedded Signature Monitoring (OESM) to perform on-line control-flow fault detection. The underlined advantage of this approach is the ability to perform a profiling algorithm that analyses the control-flow graph of user program in order to optimize the number of checkpoints (i.e., signatures) to be inserted along with the application code. By optimization, we mean to find, for a given application, the best trade-off between the minimum number of signatures to be inserted in the code, for the maximum fault detection coverage, with the minimum impact in terms of power increase. The embedded signatures are checked at runtime by the processor against compilation-time pre-computed values every time the processor reaches these signature points. Practical experiments have been carried out to demonstrate the OESM benefits when compared to conventional control-flow fault detection approaches.