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User-Centered Design Approach for Development of an Assistive Soft Exosuit and Initial Results on User Requirements
No full textTo support, and potentially improve, gait-related tasks, wearable robots (WR) are promising, especially soft-WR. To develop WR that are acceptable for their users, users need to be part of the design and development of WR. In the scope of the SWAG project, a pragmatic Human-Centered Design approach is adopted to develop a soft exosuit suitable for different applications. Accordingly, user requirements were elicited, first generally via a literature review, then specifically targeted towards SWAG via focus groups with users for each use case. Resulting user requirements feed into design and development of the exosuit, regarding the required support, modularity, donning/doffing, look and feel, and comfort. Next step is to test first mock-ups and pre-functional prototypes with users.</p
Association of pain and fatigue with health related quality of life in people with osteoarthritis
No full textOsteoarthritis (OA) is a major contributor to years lived with disability among musculoskeletal conditions, with its prevalence rising due to aging populations and increasing obesity rates. Total knee (TKA) and hip (THA) arthroplasty are common surgical interventions aimed at restoring function and improving quality of life (QoL). However, the relative contributions of sociodemographic and clinical factors to overall OoL remain insufficiently understood. This study examined the associations between pain and fatigue with the physical component summary (PCS) and mental component summary (MCS) of QoL, while controlling for functional status and sociodemographic variables. The sample consisted of 423 patients with knee OA (n = 241, mean age 65.1 ± 8 years, 40.2% male) and hip OA (n = 182, mean age 63.5 ± 10 years, 60.9% male). Correlations, multiple linear regressions, and mediation analyses were used to analyse the data. For PCS, total explained variance was 44% in TKA and 40% in THA. FS was significantly associated with PCS in both groups, with a stronger association in TKA (β = –.60, p <.001). Pain was the most significant contributor to PCS in THA patients (β = –.42, p <.001) but was not significantly related to PCS in TKA (β = –.03). For MCS, total explained variances were lower (18% in TKA and 22% in THA), with fatigue emerging as the strongest contributor in both groups (β = −.42 knee OA; β = −.48, hip OA, p <.001). Fatigue fully mediated the relationship between pain and MCS, accounting for nearly 80% of the effect. No mediation effect was observed for PCS in THA, and only partial mediation (8.4%) was found in TKA. These findings underscore the need for tailored interventions to enhance post-arthroplasty recovery and QoL improvement. Pain management should be prioritized for hip OA to improve physical well-being, whereas comprehensive, long-term strategies targeting fatigue may be may be essential for mitigating mental health burdens in both OA groups.</p
Changes in cognition, coping, pain and emotions after 12-months access to the digital self-management program EPIO
Get PDFBackground: Psychosocial pain self-management interventions can be of support for people living with chronic pain. Since psychosocial support is not always accessible, digital health interventions may increase outreach of these types of evidence-based interventions. Objectives: To explore participants' experiences from 12-month access to the digital pain self-management program EPIO, particularly in terms of any behavioral and/or psychological changes experienced. Methods: Participants (N = 25) engaged in individual semi-structured interviews following 12-month access to the EPIO intervention. Qualitative thematic analyses were conducted seeking to identify any behavioral and/or psychological changes experienced through intervention use, and what contributed to these changes. Results: Participants were predominantly women (72%), median age 46 (range 26–70), with a range of self-reported pain conditions and the majority reporting pain duration >10 years (64%). Analyses identified three main themes and subsequent sub-themes: (1) Changes in Cognition; insight and self-awareness, acceptance and shifting focus, (2) Changes in Coping; pain, emotions, and activity pacing, and (3) Content and Functionality Specific Engagement; breathing and other mind-body exercises, thought-reflection exercises, and functionalities. Conclusions: People with chronic pain experienced positive behavioral and/or psychological changes in terms of cognition and coping after 12 months access to the EPIO digital pain self-management program. The most prominent changes included increased understanding of the connection between own thoughts, feelings, and behavior, gaining concrete strategies to cope with everyday life living with pain, and utilizing these strategies to reduce pain and interference of pain, as well as to improve emotion regulation and psychological wellbeing.</p
The impact of a mainstream genetic testing pathway and socioeconomic factors on the uptake of germline genetic testing in breast cancer patients:results of the nationwide GENE-SMART study
Get PDFBackground: Genetic testing in breast cancer patients is important for the patient’s local and systemic treatment choices and follow-up, as well as for their family members. Not all eligible patients currently undergo genetic testing and disparities persist in genetic testing uptake. It is unknown on the large scale whether pre-test counselling by non-genetic healthcare professionals (HCPs)–mainstream genetic testing (MGT) – improves overall genetic testing uptake and reduces disparities. We examined the impact of MGT on germline genetic testing uptake in general and in subgroups of socioeconomic status (SES) in particular. Methods: In this retrospective nationwide cohort study, we selected all breast cancer patients from the Netherlands Cancer Registry who were eligible for genetic testing according to patient and tumour characteristics under the Dutch guidelines and who were diagnosed between 1-Jan-2017 and 31-Dec-2022. The primary outcome was genetic testing uptake. The influence of MGT and SES on overall uptake and uptake across different SES levels was evaluated using chi-squared tests and multivariable logistic regression analyses. Results: A total of 12,071 breast cancer patients were included. Overall genetic testing uptake was 67%: 78% for MGT versus 63% in referral to a genetics department (RGD) (p < 0.001) with significantly higher odds of receiving genetic testing for MGT versus RGD (OR 2.48, 95% CI 2.14–2.87). Patients with low SES showed significantly lower odds of receiving genetic testing compared to those with a high SES (OR 0.71, 95% CI 0.61–0.83). In MGT, no significant difference was found between low and high SES in the likelihood of receiving genetic testing (OR 0.75, 95% CI 0.50–1.13). Conclusions: MGT significantly increases genetic testing uptake among all eligible patients and across all SES subgroups, strongly encouraging further implementation of MGT. Educating HCPs about current disparities in genetic testing is essential to improve health equity in breast cancer care.</p
Towards Generative Governance: Co‐Creation With Emerging Technologies to Address Climate Challenges in Cities
Get PDFThis thematic issue explores how co‐creation processes, facilitated by emerging technologies, can help cities in addressing complex climate adaptation challenges. Drawing on seven interdisciplinary contributions, this issue examines the roles of digital tools, participatory methods, and institutional innovations in fostering inclusive and collaborative governance. The contributions highlight diverse approaches, ranging from computational planning support systems and interactive lighting simulations to community‐based toolkits and scenario evaluators, that are implemented across various urban contexts. Collectively, they reveal both the opportunities for and the tensions of integrating emerging technologies into co‐creation processes. This editorial identifies four key enablers of co‐creation as generative governance—interactions, tools, processes, and institutions—and offers directions for future research at the intersection of digital innovation, collaborative governance, and climate adaptation. Together, the contributions provide a deeper understanding of how cities can design and support co‐creation initiatives that are inclusive, adaptive, and capable of building long‐term capacities to address climate change challenges
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms:Towards Generalisable Neural Surrogate Models
Get PDFAbdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involves in-silico blood flow modelling with computational fluid dynamics (CFD) and extraction of hemodynamic factors like time-averaged wall shear stress (TAWSS) or oscillatory shear index (OSI). However, CFD simulations are known to be computationally demanding. Hence, in recent years, geometric deep learning methods, operating directly on 3D shapes, have been proposed as compelling surrogates, estimating hemodynamic parameters in just a few seconds. In this work, we propose a geometric deep learning approach to estimating hemodynamics in AAA patients, and study its generalisability to common factors of real-world variation. We propose an E(3)-equivariant deep learning model utilising novel robust geometrical descriptors and projective geometric algebra. Our model is trained to estimate transient WSS using a dataset of CT scans of 100 AAA patients, from which lumen geometries are extracted and reference CFD simulations with varying boundary conditions are obtained. Results show that the model generalizes well within the distribution, as well as to the external test set. Moreover, the model can accurately estimate hemodynamics across geometry remodelling and changes in boundary conditions. Furthermore, we find that a trained model can be applied to different artery tree topologies, where new and unseen branches are added during inference. Finally, we find that the model is to a large extent agnostic to mesh resolution. These results show the accuracy and generalisation of the proposed model, and highlight its potential to contribute to hemodynamic parameter estimation in clinical practice
Basal layer of granular flow down smooth and rough inclines:kinematics, slip laws and rheology
Get PDFGranular flow down an inclined plane is ubiquitous in geophysical and industrial applications. On rough inclines, the flow exhibits Bagnold's velocity profile and follows the so-called local rheology. On insufficiently rough or smooth inclines, however, velocity slip occurs at the bottom and a basal layer with strong agitation emerges below the bulk, which is not predicted by the local rheology. Here, we use discrete element method simulations to study detailed dynamics of the basal layer in granular flows down both smooth and rough inclines. We control the roughness via a dimensionless parameter, , varied systematically from 0 (flat, frictional plane) to near 1 (very rough plane). Three flow regimes are identified: a slip regime () where a dilated basal layer appears, a no-slip regime () and an intermediate transition regime. In the slip regime, the kinematics profiles (velocity, shear rate and granular temperature) of the basal layer strongly deviate from Bagnold's profiles. General basal slip laws are developed which express the slip velocity as a function of the local shear rate (or granular temperature), base roughness and slope angle. Moreover, the basal layer thickness is insensitive to flow conditions but depends somewhat on the inter-particle coefficient of restitution. Finally, we show that the rheological properties of the basal layer do not follow the rheology, but are captured by Bagnold's stress scaling and an extended kinetic theory for granular flows. Our findings can help develop more predictive granular flow models in the future
Well-posedness and stability of the Lagrange representation of the n-D wave equation via boundary triples
Get PDFWe study the Lagrange representation of the wave equation with generalized Laplacian . We allow the coefficients -- the Young modulus and the density -- to be or even nonlocal operators. Moreover, the Lipschitz boundary of the domain can be split into several parts admitting Dirichlet, Neumann and/or Robin-boundary conditions of displacement, velocity and stress. We show well-posedness of this classical model of the wave equation utilizing boundary triple theory for skew-adjoint operators. In addition we show semi-uniform stability of solutions under slightly stronger assumptions by means of a spectral result
Assessing the impact of mini environments in lithium-ion battery production under uncertainties
Get PDFDry room is one of the key cost and carbon footprint drivers in Lithium-Ion Battery (LIB) production. Therefore, reducing the burden associated with dry rooms of great significance. Mini environment concepts have been presented as a viable alternative to achieve this objective. Various studies projected its ascension in the coming years. While there are some studies discussing the technicality and potential of mini environments in battery production, there has been no attempt to quantify these potential benefits. This study, investigated the energy consumption and economic aspects of mini environments. A mini environment was introduced in the state-of-the-art LIB manufacturing process chain assessment modeling platform to replace the dry room. The impact of the mini environment was quantitatively assessed under uncertainties. The results of the study provide valuable insights for decision making on introducing mini environments in battery production.</p
Structural color sensors for lifecycle management in circular manufacturing
Get PDFCircular manufacturing seeks to extend product lifecycles and minimize waste through reuse, remanufacturing, and recycling. A key challenge in this process is effectively monitoring product conditions to optimize maintenance and ensure product longevity. However, many current monitoring systems for circular manufacturing struggle with complexity, cost, and adaptability in dynamic manufacturing environments, limiting their effectiveness. This paper explores the application of structural color-based sensor systems integrated with camera-based monitoring as a cost-effective and efficient solution for monitoring product through mechanical stress, temperatures, or environmental factors throughout the product lifecycle. These sensors change color in response to mechanical stress, temperatures, or environmental factors, providing both qualitative visual indicators and quantitative data through image processing. By converting RGB data into hue values, camera-based monitoring systems enable real-Time, scalable, and adaptable solutions for circular manufacturing processes, including lifecycle monitoring, environmental monitoring, and remanufacturing. A conceptual case study on lithium-ion battery disassembly highlights the potential of structural color sensors to enhance precision and sustainability. Integrating these systems supports streamlined decision-making, improved product quality, and waste reduction, contributing to sustainable manufacturing systems aligned with circular economy principles.</p