Abstract
The digitalization of healthcare and the widespread use of personal health technologies generate large volumes of health data, yet individuals still struggle to manage and understand their information. This paper explores the development of personas that represent typical personal health information-seeking behaviors, motivations, and needs to support the user-centered design of personal health knowledge management systems. We adopted a mixed-method approach, conducting and analyzing an online survey that collected data on personal health information needs, purposes of access, and access-related challenges, along with using affinity diagramming to group participants by key characteristics identified through data analysis. This approach derived three personas: clinical navigator, health tracker, and care planner. These personas provide a basis for designing user-centered systems that enable individuals to access, organize, and use their personal health information. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

Abstract
Stroke is a leading cause of long-term disability, often resulting in upper limb motor impairments that require long and intensive rehabilitation. For therapy and recovery after stroke, effective robotic rehabilitation requires motion control strategies that can adapt to the user in real time. However, many systems are limited by complex hardware and latency issues. This paper describes an approach that adjusts its motion control according to the user effort, in which the behavior is governed by a predictable and rules-based architecture. The system relies on the KUKA LBR iiwa’s torque sensors integrated in it’s 7 joints to monitor the user interaction and effort (TCP force) applied to the robot end-effector, requiring no extra sensing hardware. The real-time effort metric is used to modulate the stiffness of the iiwa impedance control motions, adjusting the spatial assistance or resistance provided to the user. Validation tests with impedance control on motions of around 40 cm between 2 points confirmed a predictable human-in-the-loop interaction, where dynamic stiffness adjustments reliably influence the user interaction. This approach offers a practical, safe and interpretable solution for developing reliable and easy to use robots for human upper limb rehabilitation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

Abstract
Safety-critical deployment of machine learning models requires unambiguous specification and verified implementation of tensor operators. This paper presents a formally verified workflow based on the Why3 deductive verification platform. The workflow takes each operator from an abstract WhyML specification over mathematical tensors to extracted C code, via a concrete implementation on flat arrays linked by a machine-checked refinement proof. A reusable library supports the development, providing abstract tensor types, a row-major layout with proven bijectivity, and a refinement mapping to a C-level representation. We illustrate the workflow on several ONNX operators, developed within the SONNX Working Group. Finally, we demonstrate how the abstract specifications compose to enable contract-based verification of functional properties of complete networks. The methodology is applicable beyond ONNX to any framework requiring verified tensor implementations.

Abstract
This paper proposes a distributed robotic system using multiple embedded boards, each running a real-time operating system and integrated with micro-ROS for compatibility with ROS 2 (Robot Operating System), aiming to achieve scalable, real-time distributed control, as the current implementations lack a validated, resource-aware design that achieves deterministic, low-latency synchronization across multiple microcontroller boards while integrating seamlessly with ROS 2. The boards communicate via serial connections, enabling fast, reliable, and deterministic data exchange. The proposed architecture supports parallel sensor and motor control tasks, with message synchronization through ROS 2 topics and services. The presented results demonstrate low-latency communication and real-time performance, confirming the system’s effectiveness for scalability and suitability for modular robotic applications. An alternative for using micro-ROS with the MoveIt trajectory controller is also proposed. Together, these contributions address the gap in deterministic multi-board control on commodity microcontrollers and provide a reproducible path for modular robotic platforms.

Abstract
Robotic object manipulation in industrial environments is a critical task that requires precision, safety, and adaptability, especially when handling various objects with different physical properties. This work uses a Force/Torque (F/T) sensor on the robot’s wrist to gather data on contact and object manipulation. This includes estimating the object’s mass and center of mass, detecting the object and it’s slippage, transforming F/T components to a gripping reference frame, and estimating the object’s one-dimensional (1D) orientation. The solution is applicable to industrial environments, like production lines and intralogistics, improving safety and precision. The system was implemented using Robot Operating System (ROS) and tested in a Gazebo simulation with a Universal Robot (UR) 5 manipulator, Robotiq F/T 300 sensor, and Robotiq 2F-85 gripper. The methodology was validated by manipulating three objects with distinct physical properties along four trajectories, three without slippage, demonstrating the solution’s advantages and limitations. The results confirm the success of the proposed approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

Abstract
Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Abstract Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.