Abstract
Diabetic peripheral neuropathy is a major complication of diabetes mellitus, and it is the leading cause of foot ulceration and amputations. The Semmes-Weinstein monofilament examination (SWME) is a widely used, low-cost, evidence-based tool for predicting the prognosis of diabetic foot patients. The examination can be quick, but due to the high prevalence of the disease, many healthcare professionals can be assigned to this task several days per month. In an ongoing project, it is our objective to minimize the intervention of humans in the SWME by using an automated testing system relying on computer vision. In this paper we present the project's first part, constituting a system for automatically identifying the SWME testing sites from digital images. For this, we have created a database of plantar images and developed a segmentation system, based on image processing and deep learning-both of which are novelties. From the 9 testing sites, the system was able to correctly identify most 8 in more than 80% of the images, and 3 of the testing sites were correctly identified in more than 97.8% of the images.

Abstract
Listening to internal body sounds, or auscultation, is one of the most popular diagnostic techniques in medicine. In addition to being simple, non-invasive, and low-cost, the information it offers, in real time, is essential for clinical decision-making. This process, usually done by a doctor in the presence of the patient, currently presents three challenges: procedure duration, participants' safety, and the patient's privacy. In this article we tackle these by proposing a new autonomous robotic auscultation system. With the patient prepared for the examination, a 3D computer vision sub-system is able to identify the auscultation points and translate them into spatial coordinates. The robotic arm is then responsible for taking the stethoscope surface into contact with the patient's skin surface at the various auscultation points. The proposed solution was evaluated to perform a simulated pulmonary auscultation in six patients (with distinct height, weight, and skin color). The obtained results showed that the vision subsystem was able to correctly identify 100% of the auscultation points, with uncontrolled lighting conditions, and the positioning subsystem was able to accurately position the gripper on the corresponding positions on the human body. Patients reported no discomfort during auscultation using the described automated procedure.

Abstract
Diabetes, a chronic condition affecting millions of people, requires ongoing medical care and treatment, which can place a significant financial burden on society, directly and indirectly. In this paper we propose a vision-robotics system for the automatic assessment of the diabetic foot, one the exams used for the disease management. We present and discuss various computer vision techniques that can support the core operation of the system. U-Net and Segnet, two popular convolutional network architectures for image segmentation are applied in the current case. Hardcoded and machine learning pipelines are explained and compared using different metrics and scenarios. The obtained results show the advantages of the machine learning approach but also point to the importance of hard coded rules, especially when well know areas, such as the human foot, are the systems’ target. Overall, the system achieved very good results, paving the way to a fully automated clinical system. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
One of the teacher's first goals should be to motivate students to learn. Involving students in the learning process using project-based learning could be a useful and powerful tool to prepare the students for them professional future. As part of a degree course in Biomedical Engineering, from a Portuguese University, students were asked to look at society and identify a possible failure related to the biomedical field. From the identification of this fault, the project consisted in the planning and its possible resolution. In this article we present the case study of a student's team, whose project was candidate and winner for a national prize in the field of health innovation. Despite the particularization of this case study, the students considered the experience innovative and simultaneously motivating. They also highlighted the added value of a project going beyond the classroom. Therefore, in addition to allowing them to obtain classification to the curricular unit, this type of teaching and learning could have a special impact on civil society.

Abstract
In clinical practice and in particular in the diagnostic process, the assessment of cardiac and respiratory functions is supported by electrocardiogram and auscultation. These exams are non-invasive, quick and inexpensive to perform and easy to interpret. For these reasons, this type of assessment is a constant in the daily life of a clinician and the information obtained is central to the decision making process. Therefore, it is essential that during their training, students of health-related subjects acquire skills in the acquisition and evaluation of the referred physiological signals. Simulation, considering the technological possibilities of today, is an excellent preparation tool since it exposes trainees to near real contexts but without the associated risks. Hence, the simulation of physiological signals plays an important role in the education of healthcare professionals, bioengineering professionals and also in the development and calibration of medical devices. This paper describes a project to develop synchronized electrocardiogram (ECG), phonocardiogram (PCG) and breathing sounds simulators that aims to improve an existing phantom simulator. The developed system allows, in an integrated way, to generate normal and pathological signals, being contemplated several distinct pathologies. For engineering education, it is also possible to simulate the introduction of signal disturbances or hardware malfunctions. A graphical interface allows changing operating parameters in real time.

Abstract
This study aims to develop a new jig holding system that is able to be controlled by a Computer Numeric Control (CNC) installed on three-axis machining centers, which can drastically im-prove the productivity in machining operations, enabling the machining of unparallel plans in the same setup. An action research methodology was adopted for this work, which, through a practical approach, intends to generate transferrable knowledge to other organizations whose situations are like those in this study. Together, the practical actions and the knowledge acquired create the changes needed for improving these processes. By conducting a case study, it was observed that savings of about 40 % can be easily achieved for parts with low geometric complexity. If the complexity of the parts increases, it is expected that these savings can be even higher. The return of investment is less than 2 years, which is usually affordable for enterprises. Through this study, it was possible to develop a new jig holding system that can be attached to a three-axis CNC machining center and clearly expands its functions and productivity. With this system, it is possible to work in different planes of the part in sequence, as well as use a double-sided table for the jigs, doubling the production batch each time the machine is loaded. Moreover, a list of key settings has been created with the main requirements and recommendations to adopt this kind of production system, which can be highlighted as the main research output.