With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
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BACKGROUND: Although enhancing physical activity (PA) is important to improve physical and/or cognitive recovery, little is known about PA of patients admitted to an inpatient rehabilitation setting. Therefore, this study assessed the quantity, nature and context of inpatients PA admitted to a rehabilitation center. METHODOLOGY/PRINICIPAL FINDINGS: Prospective observational study using accelerometry & behavioral mapping. PA of patients admitted to inpatient rehabilitation was measured during one day between 7.00-22.00 by means of 3d-accelerometery (Activ8; percentage of sedentary/active time, number of sedentary/active bouts (continuous period of ≥1 minute), and active/sedentary bout lengths and behavioral mapping. Behavioral mapping consisted of observations (every 20 minutes) to assess: type of activity, body position, social context and physical location. Descriptive statistics were used to describe PA on group and individual level. At median the 15 patients spent 81% (IQR 74%-85%) being sedentary. Patients were most sedentary in the evening (maximum sedentary bout length minutes of 69 (IQR 54-95)). During 54% (IQR 50%-61%) of the observations patients were alone) and in their room (median 50% (IQR 45%-59%)), but individual patterns varied widely. CONCLUSION/SIGNIFICANCE: The results of this study enable a deeper understanding of the daily PA patterns of patients admitted for inpatient rehabilitation treatment. PA patterns of patients differ in both quantity, day structure, social and environmental contexts. This supports the need for individualized strategies to support PA behavior during inpatient rehabilitation treatment.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
Design, Design Thinking, and Co-design have gained global recognition as powerful approaches for innovation and transformation. These methodologies foster stakeholder engagement, empathy, and collective sense-making, and are increasingly applied to tackle complex societal and institutional challenges. However, despite their collaborative potential, many initiatives encounter resistance, participation fatigue, or only result in superficial change. A key reason lies in the overlooked undercurrent—the hidden systemic dynamics that shape transitions. This one-year exploratory research project, initiated by the Expertise Network Systemic Co-design (ESC), aims to make systemic work accessible to creative professionals and companies working in social and transition design. It focuses on the development of a Toolkit for Systemic Work, enabling professionals to recognize underlying patterns, power structures, and behavioral dynamics that can block or accelerate innovation. The research builds on the shared learning agenda of the ESC network, which brings together universities of applied sciences, design practitioners, and organizations such as the Design Thinkers Group, Mindpact, and Vonken van Vernieuwing. By integrating systemic insights—drawing from fields like systemic therapy, constellation work, and behavioral sciences—into co-design practices, the project strengthens the capacity to not only design solutions but also navigate the forces that shape sustainable change. The central research question is: How can we make systemic work accessible to creative professionals, to support its application in social and transition design? Through the development and testing of practical tools and methods, this project bridges the gap between academic insights and the concrete needs of practitioners. It contributes to the professionalization of design for social innovation by embedding systemic awareness and collective learning into design processes, offering a foundation for deeper impact in societal transitions.
This proposal aims to explore a radically different path towards a more sustainable fashion future through technology. Most research on fashion and technology focuses on high tech innovation and, as a result, overlooks knowledge that is already available and has been used, tested and improved for centuries. The proposed research project, however, looks backward to move forward. It aims to investigate ‘the blindingly obvious’ and asks the question how historical technologies could be used to solve contemporary environmental issues in fashion. It thus argues that technology from the past could inspire both designers and technologists to come up with new and exciting solutions to make the future of fashion more sustainable. The current fast fashion system has changed the relationship consumers have with their clothing. Clothing has become a throwaway object and this has severe environmental implications. This research project aims to find a solution by exploring historical technologies - such as folding, mending and reassembling-, because in the past a ‘sustainable’ attitude towards fashion was the norm simply because cloth and garments were expensive. It wants to examine what happens when consumers, fashion designers and technologists are confronted with these techniques. What would, for example, materialize when an aeronautical engineer takes the technique of folding as a starting point and aims to create clothes that can grow with babies and toddlers? The answer is the signature suit of the brand Petit Pli: a special folding technique allows their signature suit to grow with children from 3 months to 3 years. Much like the age-old folding techniques applied in traditional Dutch dress, which allowed the size women’s jackets to be altered, by simply adjusting the pleats. Similarly, this project aims to investigate how high tech solutions, can be initiated through historical techniques.
