The sensation of tactile slip is ubiquitous to dexterous in-hand manipulation and plays a central role in grip control and surface perception. Replicating these sensations artificially is a valuable tool for increasing embodiment and immersion in robotic teleoperation and virtual reality applications. Existing slip-rendering devices span a wide range of designs, including vibrotactile, belt-, platform-, and tactor-based systems, each involving varying degrees of mechanical complexity. The present device elicits vivid tactile slip illusions through a minimalist architecture of only two independently actuated interleaved pin arrays [1], [2]. When driven in anti-phase, the arrays produce a differential motion mode that reliably evokes the sensation of a surface slipping beneath the finger. Building on this platform, the present work investigates whether vibration frequency can serve as an independent control parameter for perceived slip speed, independent of amplitude. A two-alternative forced-choice psychophysical experiment was conducted with 30 participants. In the motion detection part of the experiment, participants reliably identified the anti-phase stimulus as motion at 70 Hz and 90 Hz (both p < .001), while performance at 50 Hz did not exceed chance, attributed to hardware resonance. In the speed discrimination part of the experiment, participants consistently perceived frequencies above the 70 Hz reference as faster (p < .001 at 80 and 90 Hz), while four participants showed an inverted frequency–speed relationship. These results support frequency variation as a viable encoding strategy for perceived slip speed in dual-array tactile displays.

Background and research objective - Approximately 80\% of stroke patients experience upper limb motor impairments, including spasticity, muscle weakness, and abnormal synergies, which complicate daily activities. Accurate quantification of motor impairments is essential for optimizing rehabilitation strategies after a stroke. Objective and automatic assessment can be achieved with robotic devices. The Shoulder Elbow Perturbator (SEP) applies controlled mechanical perturbations to assess elbow and shoulder impairments. During these measurements, the wrist is fixated using plastic clamps with foam padding. But these clamps cause discomfort, positioning inconsistencies, instability, and unintended hand movemen. This study aims to design and evaluate an alternative fixation method for securing the arm in the SEP.

Method and results - A design thinking approach was applied. During brainstorming, concepts focused on arm conformity, rigidity, load distribution, and unrestricted elbow rotation. Based on a Harris profile, a lightweight vacuum brace was developed into a functional prototype, and clamps were redesigned to fit the brace. Validation experiments were performed with eight healthy participants tested in the SEP with both the vacuum brace and original rigid clamps. Comfort was assessed via questionnaire. Additionally, SEP angle and torque, and arm motion capture data were collected. The vacuum brace significantly reduced discomfort from severe to slight. SEP and motion capture data showed comparable fixation stability during multisine tasks. During passive stretch trials, lower intraclass correlation coefficients for stiffness estimation increased elbow displacement, and occasional movement within the clamps were observed with the brace, indicating reduced fixation performance.

Conclusions - The vacuum brace improved participant comfort during robotic assessment of upper limb motor impairments. Fixation stability was comparable during multisine perturbation tasks but reduced during stretch tasks, likely due to the current clamp design.

Objective: This study aimed to validate the use of Inertial Measurement Units (IMUs) for quantifying trunk motion in wheelchair sports and explore the relationship between trunk motion, wheelchair mobility performance, and classification level in elite wheelchair basketball athletes.
Methods: Fourteen participants (seven elite wheelchair athletes and seven non-WC users) completed eight standardized wheelchair mobility tests. Trunk motion was measured using IMUs and compared with a 3D motion capture (MOCAP) system. Trunk kinematics and wheelchair performance metrics were derived and analysed for correlation in the elite wheelchair group.
Results: IMUs, especially those mounted on the upper back, showed excellent agreement with 3D motion capture data (ICCs > 0.90; RMSE < 8%). Mean trunk angle correlated with wheelchair linear velocity during manoeuvring and sprinting tasks (r > 0.75), highlighting the role of dynamic trunk use in enhancing propulsion. Additionally, trunk kinematics, particularly range of motion, mean tilt angle, and forward lean time, showed strong correlations with classification level (r > 0.79), especially during the straight push test. Performance metrics demonstrated weaker correlation with classification (r < 0.47), suggesting that impairment may not directly translate into measurable differences in mobility performance in this sample.
Conclusion: Trunk rotation angles derived from IMUs provide a valid and practical tool for measuring trunk motion. Their integration could support more transparent and impairment-focussed classification frameworks.

Climate change is an urgent global challenge. Buildings significantly contribute to energy consumption and CO2 emissions. To accelerate decarbonization, ATES systems, integrated with 5GDHC networks, offer a sustainable approach. These systems enable seasonal thermal energy storage and bidirectional energy exchange. However, designing these complex combined systems in urban areas poses significant challenges, including integrating dispersed ATES wells, managing phased construction, and navigating dense underground infrastructure. This study investigated a biologically inspired approach, utilizing the network optimization capabilities of slime mould (Physarum polycephalum). A simulation model was developed and verified, incorporating key slime mould behaviours like efficient pathfinding, to generate optimal thermal network configurations. Using the TU Delft Campus as a case study, this research explored the model's capacity to streamline the design of sustainable and efficient heat distribution systems. Key findings indicate that the visual differences in layout between the best results and all results are minimal. In conclusion, this research successfully developed and applied a slime mould growth model for optimal thermal network design, specifically for district heating systems combined with ATES in an urban environment. The model proved effective in generating a network designs that minimize CapEx.

This study presents a novel movable ankle in a Lower Limb Paralysis Simulator (LLPS) and investigates how incorporating a movable ankle into a LLPS influences gait performance in able-bodied users. Three ankle configurations of the new movable ankle were assessed and compared with a rigid condition during walking trials with three able-bodied participants. Outcome measures included stride length, walking speed, physiological cost index, crutch force, and user-perceived stability. The movable ankle conditions produced walking speeds higher than the rigid condition. The physiological cost index was reduced across all movable configurations, and participants reported improved stability and control. Due to the small sample size, further testing must be conducted to see significant results. However, this study shows the potential of integrating a movable ankle in a LLPS to improve gait outcome measures.

Aseptic pharmaceutical manufacturing demands ultra-clean automation to safeguard product integrity and patient safety, yet traditional rigid body mechanisms generate particles, require lubricants, and are difficult to sterilize. Compliant mechanisms (CMs), achieving motion through elastic deformation of monolithic structures, offer a compelling alternative by inherently eliminating these contamination sources. However, their feasibility within the strict constraints of Grade A aseptic environments remains underexplored.

This Master's thesis investigates the design, analysis, and preliminary validation of a flexure-based compliant gripping mechanism for sterile handling of pharmaceutical components, developed in collaboration with Novo Nordisk. The research employed a systematic approach, integrating analytical stiffness modeling (including refined corner-filleted arc leaf flexures), Pseudo-Rigid Body Models (PRBMs) for gripper kinematics, and Finite Element Analysis (FEA) for stress and kinematic performance. A cleanroom-compatible voice coil actuator (VCA) drove the system, which included a compliant linear guide mechanism (LGM) and a two-fingered gripping mechanism (GM), complemented by an aseptic bayonet locking system. Preliminary validation involved prototyping (in Al7075) and cleanability assessment via riboflavin testing.

Results demonstrated strong agreement between analytical (13.00 N/mm) and FEA (13.45 N/mm) predictions for LGM stiffness, validating the accuracy of the refined flexure models. Modal analysis confirmed robust dynamic stability, with the first axial mode (84.196 Hz) well separated from parasitic modes. The GM achieved a 7.5 mm gripping motion from a 4 mm VCA input, with optimized tapered flexures reducing peak von Mises stresses by approximately 35$\%$. The monolithic design inherently eliminated particle-generating elements, and riboflavin tests showed high cleanability, identifying minor geometric refinements for optimal aseptic compliance. The design was subsequently re-optimized for pharmaceutical compatible material Super Duplex, preserving validated performance.

This study successfully demonstrates the viability of compliant mechanisms as a critical enabling technology for next generation automation in aseptic pharmaceutical manufacturing. By inherently minimizing particle generation, simplifying sterilization, and ensuring cleanroom compatibility, these mechanisms promise enhanced sterility assurance and improved efficiency, paving the way for advanced pharmaceutical production.

Introduction: Movement disorders, such as Cerebral Palsy, prevent children from fully developing their motor coordination abilities reducing their quality of life. Trunk control is a core coordination competency but there is limited variety in sitting based therapies. This paper investigates a theorised trunk support device for children which uses a system of elastic ropes and pulleys to support and prevent them from falling whilst they perform trunk control training exercises in a seated position. The aim of this study is to understand the mechanical properties required to ensure this device behaves safely and predictably. The device is considered safe if it prevents the child from falling and predictable if it always pulls the child towards an upright seating position. Additionally, it was investigated if the mechanical properties could be satisfied by off-the-shelf components and two possible pulley systems were compared.

Methods: To theoretically test if this device concept could meet the three criteria, a MATLAB simulation was written. A genetic optimization was then used to find the least stringent mechanical properties that still satisfy the three criteria. Off-the-shelf components that satisfy the mechanical properties were then found.

Results: Both pulley systems satisfied the safety and predictability criteria. Moreover, the necessary mechanical properties could be satisfied by off-the-shelf components, though some design modifications may be required. Ultimately, the configuration involving shorter ropes was determined to be the better option as it had less stringent mechanical requirements and potential design modifications would likely be easier to implement.

Conclusion: This study concluded that the optimised device, theoretically, shows good performance and seems practical to build. Future work is required to design a harness that would allow a child to comfortably train with the device.

Introduction
Acquired Brain Injury (ABI) presents a significant public health challenge due to the diverse recovery trajectories resulting from its heterogeneous nature. Prediction models, derived from structured data collection, offer a more personalised approach to neurorehabilitation. However, a substantial gap remains between the development of these models and their successful implementation in clinical practice. This study addresses this gap by focusing on two key components: externally validating prediction models for functional independence in young adults with ABI, and creating a user-friendly interface to support their application in clinical settings. These efforts represent a crucial step toward advancing precision neurorehabilitation, enabling data-driven, individualised care tailored to the unique needs of ABI patients.

Design and Methods
Previously, three multivariable prediction models were developed to predict the prognosis of severe ABI in young adults aged 16 to 35 admitted to the Daan Theeuwes Centre, demonstrating promising performance. These models focused on functional independence, measured by the Barthel Index (BI) at admission, three months later, and the change in independence during this period. This study focused on the implementation of these prediction models by external validation of these models, and the development of a web-based tool to facilitate their implementation in clinical practice. Data for the external validation cohort were sourced from the Measurement Feedback System (MFS). Highly incomplete variables were excluded, and missing data were handled using Predictive Mean Matching (PMM). Model performance was assessed using Coefficient of Determination (R2), Root Mean Square (RMSE), and Mean Absolute Error (MAE), alongside calibration and correlation analyses. Additionally, results were assessed against the 95% Prediction Interval (PI) of the development cohort. A web-based tool was developed simultaneously to facilitate the practical application of these models in clinical practice, informed by clinician feedback and literature insights.

Results
The validation cohort (n = 21) showed minimal discrepancies compared to the development cohort (n = 100), but external valida- tion revealed reduced predictive accuracy. The ”Level of Independence at Admission,” ”Level of Independence at Three Months Post-Admission” and ”Change in Independence over Three Months” models had notable drops in R2 from 65.7% to 42.8%, 59.3% to 29.7%, and 76.3% to 35.9%, respectively. All models fell outside the 95% Confidence Interval (CI) of R2 for the development cohort and showed increased RMSE and MAE values. Calibration showed overestimation of lower BI scores and underestimation of higher scores, with a substantial proportion of predictions falling outside the 95% PI of the development cohort. Correlation analysis indicated that longer hospital stays and Post-Traumatic Amnesia (PTA) were linked to higher prediction errors, while higher BI scores at admission and focal injuries in Traumatic Brain Injury (TBI) were associated with lower errors. The web-based tool included a page for applying the models, one for visualising recovery trajectories via an interactive flow diagram, and another for accessing detailed model information.

Discussion
The implementation of prediction models involves several key phases, beginning with structured data collection, model devel- opment, and evaluation. Once the model demonstrates sufficient performance, it can be implemented into clinical practice. To maintain its relevance, continuous monitoring and updating are essential. In this study, we focused on two main components— external validation of the prediction models and the practical implementation through the development of a user-friendly tool. By addressing these, we have taken significant steps towards implementing prediction models into clinical practice. These components underscore the importance of structured data collection, rigorous validation, and practical application to ensure the models’ effectiveness in real-world settings. By implementing prediction models, we aim to employ a data-driven approach that brings us closer to precision neurorehabilitation.

Humans perceive a pulling or pushing sensation when subjected to an asymmetric vibration. This so-called pseudo force has great potential to guide human movement. Previous research has exclusively focused on the effect of pseudo forces in open-loop environments, in which the user’s joint angular velocity cannot be corrected. As the latter is essential for providing movement guidance, this paper proposes the first closed-loop system in the field of pseudo forces, using amplitude-modulated pseudo forces as haptic feedback. With this feedback, the user was assisted in moving towards a specific target angular velocity. In a human factors experiment, the amplitude-modulated stimuli were compared to constant-amplitude stimuli. The results showed that amplitude-modulated pseudo forces significantly decreased the error between the user’s and the target angular velocity when continuous movement in the desired direction was achieved. Therefore, the study demonstrated that amplitude-modulated pseudo forces can effectively guide human movement, representing an essential step towards developing a wearable movement guidance device.

Automation within an industrial sector has now for years been an obvious factor. Everywhere mechanical instruments and sensors are implemented for fast and precise processes. The agricultural sector is some steps behind in relation to other sectors. Reason being that there are many irregularities which in other sectors have been made into uniform variables. One of the problems that needs to be tackled within the agricultural sector is having the leaves of crops removed.
Rijk Zwaan has created a new variety of broccoli and to make this commercially viable, a harvest machine is required. One of the aspects of this harvesting process is removing the leaves. This has to be achieved in one continuous session, with at least a ground speed of 1 km/h, while the broccoli's are still in the field. With a focus on the best performance for the least destructive working principle. After listing all the requirements, ideas are structured in a morphological overview. A weighted criteria table will be used to select three ideas, which will be made into prototypes.
Results from the initial testing period will reveal the best concept, which will be optimised. This was the Metal Wire design, which exist of multiple stainless steel wires attached perpendicular to a rotating shaft. An important aspect that boosted the performance was the unravelled ends of these wires. These rotating shaft had the wires removing the leaves, while beating the leaves down from both sides.
After optimization, the current prototype could ensure a 60% of the top section and 80% of the middle section with 20% of the broccoli’s destroyed. Which was not enough to satisfy the goal, however the promising results could be improved with an upgraded machine. A clear trade-off was found between the performance and destruction. Significant improvements in results could be achieved by incorporating additional features such as a funnel and designing a more robust structure with fewer obstructions, controllable ground/rotational speed, and better alignment with the beds. Therefore at the current state the leaf removal machine operates substandard, but has potential to be developed into the desired product.

The provision of somatosensory information may play a fundamental role in the recovery of stroke patients. Particularly, tactile information is known to influence motor control by contributing to the perception of the weight, friction, and slip condition of objects. Despite this, the inclusion of tactile information through haptic rendering in robotic neurorehabilitation systems remains largely unexplored. In this study, we present a tactile interface to extend the kinesthetic rendering capabilities of an existing hand rehabilitation robot. The developed solution relies on skin stretch stimulation of the fingerpads, which allows the rendering of interaction forces with tangible virtual objects, e.g., friction, weight, and inertia. In contrast with previous skin stretch devices, our system uses closed-loop force control for accurate force rendering, relying on a custom magnetic field-based three-axis force sensor. A three-axis positioning stage in combination with a reference force sensor was used for calibrating and characterizing the sensor, as well as evaluating the interface response. The sensor achieves shear force accuracies of 0.2 N, influenced by hysteresis and viscoelastic creep effects. The tactile interface achieves a steady-state error of 0.2–0.4N and rise times of 20–70 ms during step response tests. Frequency response tests show that the interface can successfully track signals up to 5–7 Hz. The novel use of force-controlled skin stretch stimulation aims to open a new avenue for the accurate rendering of interaction forces through the tactile sense. Moreover, through purposeful design for usage in the rehabilitation domain, we hope that this study will serve as a stepping stone toward the inclusion of tactile information in robot-assisted therapies.

Learning from Demonstration (LfD) aims to learn versatile skills from human demonstrations. The field has been gaining popularity since it facilitates transferring knowledge to robots without requiring much expert knowledge. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized (TP) learning encodes relevant contextual information in reference frames, enabling better skill generalization to new situations. However, most TP learning algorithms require multiple demonstrations in various environment conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel concept to learn motion policy from few demonstrations through explicitly solving reference frame weights along the task trajectory. Experimental results in both simulation and real robotic environments validate our approach.