Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train’s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leixões line. This real-world testing scenario allowed the evaluation of the framework’s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks.

This paper presents a practical and cost-effective method for in-motion estimation of a vehicle’s CoG position in all three directions by measuring accelerations during two types of maneuvers: braking (longitudinal and vertical CoG estimation) and cornering (lateral and vertical CoG estimation). The proposed method’s main advantage is that it does not require knowledge of vehicle characteristics, such as mass distribution, suspension geometry, or inertia parameters. It relies solely on the known distances between the sensors and their positions relative to a defined reference point on the vehicle. To validate the developed method, experimental tests were conducted on a prototype vehicle, varying the load conditions for the proposed driving scenarios. The CoG position obtained from dynamic maneuvers was compared with reference values derived from static measurements. The results showed that the proposed method could estimate the CoG position with an average error of 3% in the longitudinal direction, a maximum error of 12% in the lateral direction, and a maximum error of 14% in the vertical direction.

Hand exoprosthesis are commonly assumed as a promising approach to help people regain independence after upper limb losses. Injury-related data from recent years highlights the need to continue developing solutions to increase end-user acceptance. Within this scope, the present review aims to provide up-to-date information related on advancements and current trends in hand exoprosthesis development. Following a PRISMA methodology, 60 studies were included in this review covering a different range of actuation strategies and design approaches. The main features of the devices developed in the literature are also presented in detail. Concerning actuation strategies, linkage-driven and tendon-pulley mechanisms are the most common approaches presented in the literature, however different strategies such as twisted-string actuators differential mechanisms are also proposed. In turn, pneumatic and hydraulic actuation approaches are also presented as soft alternatives to electric motors. Passive elements such as springs or clutch mechanisms are frequently employed to achieve underactuation in these devices. 3D Printed technologies are also suggested as alternatives to the most conventional manufacturing methods. By covering all these topics, the present review is meant to provide useful insights into future developments in this field. End-user-oriented continuous improvement and the development of highly anthropomorphic solutions are still current challenges, that should be addressed in upcoming developments. This work was developed in the scope of the project ‘NerveRepack—Intelligent neural system for bidirectional connection with exoprostheses and exoskeletons’, which has received funding from the Horizon Europe RIA programme under grant agreement N^∘ 101112347.

Prosthetic hands have seen significant improvements in recent years, enabling increasingly more natural interactions between patients with upper limb loss and their environment. Nonetheless, progress is continuously being made to enhance user acceptance, which remains a major drawback in such systems. The efficiency of the actuation mechanism is a critical parameter when designing these devices. Maximising actuation approach efficiency enables the use of smaller and lighter motors, thus decreasing the overall weight of the solution. Simultaneously, increased efficiency contributes to more precise motor control. Within this context, the present work introduces a novel actuation concept. Conventional tendon–pulley mechanisms are often susceptible to tendon slippage; therefore, a hobbed tendon–pulley approach was investigated to maintain cable tension more consistently and efficiently. This approach aims to provide smoother operation, improved reliability, and a reduced risk of mechanical failure due to tendon slippage. Simultaneously, the capability of holding and maintaining a set force is of utmost importance in these systems, and the force-feedback system is usually a major concern. The present work also focuses on comparing current and pressure-based control methodologies for the developed prosthesis. The current-based approach had the significant advantage of not requiring external sensors to be assembled on the prosthesis and not relying on the point of application of force being inside the sensor’s active area. During these tests, the prosthesis successfully grasped various objects of different sizes, shapes, stiffnesses, and weights using a current-based approach, without observable tendon slippage.