Recommender systems are increasingly used in mobile health interventions, such as managing Chronic Musculoskeletal Pain (CMP). While researchers have highlighted the importance of explaining health-related recommendations to lay users, with benefits such as increased trust and a higher tendency to follow up on these recommendations, how to design explanations for lay users in critical contexts such as health remains largely unexplored. To address this gap, we develop a mobile health application to support users with CMP through coaching and personalised health recommendations delivered via a conversational rule-based recommender system. This article describes the three-phase iterative development of the RS, involving health experts and end users. In the first iteration, we conduct a preliminary validation study with \(N=282\) participants to ensure the app’s validity and improve the initial set of health recommendations. Next, two user studies are conducted centred around designing effective and understandable explanations for these recommendations. First, we design six explanation modalities tailored towards lay users, and through a qualitative study ( \(N=11\) ), extract initial design guidelines for explaining health recommendations, finding a strong preference towards feature importance explanations and identifying issues with modalities that highlight negative emotions. Given these results, we explore whether extending feature importance explanations with textual information into a ‘hybrid’ explanation could benefit end users, and whether these benefits depend on a user’s personal characteristics (need for cognition and ease-of-satisfaction). Through a mixed-methods study with \(N=262\) participants, we find that the hybrid modality significantly increased user trust, transparency, persuasiveness, usefulness and satisfaction compared to unimodal explanations. However, users with a higher need for cognition rate unimodal explanations more positively than hybrid ones.

This paper explores the adoption of a clinical decision support system (cDSS) utilizing game-based digital biomarkers for diagnosing mild cognitive impairment (MCI). Specifically, it investigates how different explanation methods, with a focus on data-centric explanations, impact perceived ease-of-use, perceived usefulness, and trust among healthcare professionals (HCPs). Through a qualitative study with 12 HCPs, we assess their interactions with an explainable AI (XAI)-enriched cDSS. The findings indicate that HCPs are open to adopting XAI-enriched cDSS to communicate the outcomes of game-based digital biomarkers. HCPs preferred to receive key diagnostic information in an easily digestible format. Both local explanations of intra-personal evolutionary data and global overview of normative data were found to be valuable for interpreting digital biomarkers. HCPs tended to trust the machine learning algorithms as a black box, but they considered the dataset used for training the model and the outcome prediction to be crucial. Therefore, presenting the uncertainty alongside the prediction was deemed important. These insights underscore the importance of designing cDSS tools that foster trust through clear, actionable explanations, paving the way for improved decision-making in clinical contexts.

Identifying explanation needs based on user characteristics has been the focus of human-centred research within XAI for some time.In Ribera et al. 's proposal of user-centred XAI, expertise was used as a proxy for characterising the user, and in turn guide explanation design.Since then, the research landscape has evolved to include a broader notion of stakeholders, ranging from AI developers to external regulators to the affected users of AI decisions.However, with this broadening of stakeholder roles, there emerged a pattern of conflating expertise and role, such as the term "end user" being used interchangeably for domain experts using (X)AI for decisionmaking and lay users impacted by AI decisions, with both having vastly different explanatory needs.In this work, we revisit previous surveys with the aim to identify and classify stakeholders in the XAI ecosystem.We propose to consistently categorise stakeholders along separate expertise and role dimensions.By disentangling both, we present a framework that highlights the diversity of stakeholder goals and the challenges of aligning explanation design with varied user requirements.Our analysis maps stakeholders onto these dimensions and discusses how using both expertise and role can inform the development of more tailored and effective XAI solutions.

sponsorship: We would like to thank ZAVO, and Joke Vandepitte in particular, for allowing us to collaborate. We would like to thank FWO by facilitating this interdisciplinary research. Additionally, we would like to thank all participants for their time and valuable insights. This research is part of the research projects funded by KU Leuven (grant C14/21/072) and the Flanders AI Research Program (FAIR). (KU Leuven|C14/21/072, Flanders AI Research Program (FAIR))

Utilizing a validated scale to assess player experience (PX) in video games can yield important findings. Nevertheless, insufficient quantitative data analysis expertise among games user research (GUR) self-taught practitioners, poses obstacles to effectively analyzing and interpreting the data obtained from various measurement scales. Hence, in this paper, we present a guided interface based web platform to support data analysis of the Player Experience Inventory (PXI), following a user-centered design (UCD) process, incorporating insights from Games User Research (GUR) specialists. The interface, extending a current platform for PXI data evaluation, aspires to enable data analysis with a high degree of flexibility, offering explanatory support for both, inexperienced and experienced analysts. The findings from user testing sessions indicated that the platform was perceived as user-friendly. However, findings also suggest that more accessible explanations of the statistical approaches are needed to aid in understanding data analysis and interpretation of outcomes.

Recent advancements in Educational AI have focused on models for automatic question generation. Yet, these advancements face challenges: (1) their "black-box" nature limits transparency, thereby obscuring the decision-making process; and (2) their novelty sometimes causes inaccuracies due to limited feedback systems. Explainable AI (XAI) aims to address the first limitation by clarifying model decisions, while Interactive Machine Learning (IML) emphasises user feedback and model refinement. However, both XAI and IML solutions primarily serve AI experts, often neglecting novices like teachers. Such oversights lead to issues like misaligned expectations and reduced trust. Following the user-centred design method, we collaborated with teachers and ed-tech experts to develop an AI-aided system for generating multiple-choice question distractors, which incorporates feedback, control, and visual explanations. Evaluating these through semi-structured interviews with 12 teachers, we found a strong preference for the feedback feature, enabling teacher-guided AI improvements. Control and explanations’ usefulness was largely dependent on model performance: they were valued when the model performed well. If the model did not perform well, teachers sought context over AI-centric explanations, suggesting a tilt towards data-centric explanations. Based on these results, we propose guidelines for creating tools that enable teachers to steer and interact with question-generating AI models.

Repeated measurements of player experience are crucial in games user research, assessing how different designs evolve over time. However, this necessitates lightweight measurement instruments that are fit for the purpose. In this study, we conduct an examination of the test-retest reliability of the miniPXI -a short variant of the Player Experience Inventory (PXI) , an established measure for measuring player experience. We analyzed test-retest reliability by leveraging four games involving 100 participants, comparing it with four established multi-item measures and single-item indicators such as the Net Promoter Score ( NPS ) and overall enjoyment. The findings show mixed outcomes; the miniPXI demonstrated varying levels of test-retest reliability. Some constructs showed good to moderate reliability, while others were less consistent. On the other hand, multi-item measures exhibited moderate to good test-retest reliability, demonstrating their effectiveness in measuring player experiences over time. Additionally, the employed single-item indicators ( NPS and overall enjoyment) demonstrated good reliability. The results of our study highlight the complexity of player experience evaluations over time, utilizing single and multiple items per construct measures. We conclude that single-item measures may not be appropriate for long-term investigations of more complex PX dimensions and provide practical considerations for the applicability of such measures in repeated measurements.

Fearing stigma, parents often hide their children's HIV diagnosis from them, and postpone disclosure, in turn negatively impacting children's well-being. Our work explores whether interactive technology can support disclosure. In the first study, we examine disclosure experiences and the role of interactive technology from the perspective of HIV-positive children and parents. Through Thematic Analysis, we highlight how disclosure is linked with parents’ own experience of HIV, and that disclosure needs to be viewed as a process. On this basis, we contribute an experience prototype that guides parents through an incremental disclosure process using interactive storytelling. In a second study, we evaluate the prototype through interviews with six parents. Leveraging Interpretative Phenomenological Analysis, we show that the prototype has potential to transform how parents understand and approach disclosure. Based on our results, we present further design directions, and discuss the (limitations of the) role that technology can play in this context.

Gamification is a popular technique to improve task engagement, and has broadly been deployed in health and education to a point where many users now expect gameful experiences in these settings. However, gamification has been criticised for being a potential obstacle to the experience of reflection. Motivated by this tension, our work examines how the addition of gamification to a Virtual Reality simulation of breastfeeding impacts player experience and reflection. Using a within-subjects design, we invited 34 participants to take part in a mixed-methods evaluation of a gamified and non-gamified variant of the simulation that included questionnaires and semi-structured interviews. Results show that gamification improved player experience and encouraged players to reflect on goal achievement and performance. However, it also diverted players’ attention from nuances within the act of nursing. Drawing on our findings, we contribute considerations for the application of gamification in personal and sensitive settings such as breastfeeding.

Wearable activity trackers are inaccessible to older adults who use mobility aids (e.g., walker, wheelchair), because the accuracy of trackers drops considerably for such movement modalities (MMs). As an initial step to address this problem, we implemented and tested a minimum distance classifier to automatically identify the used MM out of seven modalities, including movement with or without a mobility aid, and no movement. Depending on the test setup, our classifier achieves accuracies between 82 % and 100 %. These findings can be leveraged in future work to combine the classifier with algorithms tailored to each mobility aid to make activity trackers accessible to users with limited mobility.

Adolescents in sub-Saharan Africa are at the epicenter of the global HIV epidemic. Yet, technology to support HIV management overwhelmingly focuses on adult medication adherence, neglecting the complex lives of adolescents in low-income regions. We present findings from interviews and focus groups that included twelve HIV-positive adolescents in Ethiopia, and eleven adults from their care circles. We leverage the Integrated Behavioral Model to examine the lived experience of HIV and the space for technology. Additionally, we present an inductive thematic analysis, which highlights non-disclosure as a central theme, i.e., adolescents remaining unaware of their HIV status. Drawing from these findings, we discuss how to account for (the lack of) disclosure in the design of technology to support HIV management, and reflect on whether technology could (and should) support the process. We further highlight the risks that researchers and designers need to be aware of when designing HIV management technology for this audience.

Maker culture encourages do-it-yourself practices to create, repair, and repurpose technology. In Human-Computer Interaction (HCI) research, it is seen as a means of empowering people, providing affordable and customisable technology with potential to enrich areas such as education or assistive technology. To investigate this alleged potential, we performed an anthropological inquiry at an elementary school for disabled children that lasted one year, participating in everyday activities with students, teachers, and therapists. We observed ‘heterogeneity in a fluid environment’ and ‘creativity in the moment’ in an ‘endemically underfunded’ setting. We saw how technology is ‘injecting dependencies’, ‘reinforcing disability’, and ‘occupying time and space’, changing our view on the role that making can have. Leveraging Empowerment Theory, we highlight how (making) technology risks ignoring the intertwined dynamics between the individual, the organisational, and the community, and articulate points for reflection for technology in schools for disabled children for the HCI research community.

Recommender systems are increasingly used in high-risk application domains, including healthcare. It has been shown that explanations are crucial in this context to support decision-making. This paper explores how to explain call recommendations to nursing home staff, providing insights into call priority, notifications, and resident information. We present the design and implementation of a recommender engine and a mobile application designed to support call recommendations and explain these recommendations that may contribute to residents’ safety and quality of care. More specifically, we report on the results of a user-centered design approach with residents (N=12) and healthcare professionals (N=4), and a final evaluation (N=12) after four months of deployment. The results show that our design approach provides a valuable tool for more accurate and efficient decision-making. The overall system encourages nursing home staff to provide feedback and annotate, resulting in more confidence in the system. We discuss usability issues, challenges, and reflections to be considered in future health recommender systems.

Artificial intelligence (AI) has seen a steady increase in use in the health and medical field, where it is used by lay users and health experts alike. However, these AI systems often lack transparency regarding the inputs and decision making process (often called black boxes), which in turn can be detrimental to the user’s satisfaction and trust towards these systems. Explainable AI (XAI) aims to overcome this problem by opening up certain aspects of the black box, and has proven to be a successful means of increasing trust, transparency and even system effectiveness. However, for certain groups (i.e. lay users in health), explanation methods and evaluation metrics still remain underexplored. In this paper, we will outline our research regarding designing and evaluating explanations for health recommendations for lay users and domain experts, as well as list a few takeaways we were already able to find in our initial studies.

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. Therefore, centering on transparency and control, we analyzed how 18 users of movie recommender systems perceived four different TUIs using individual co-design sessions and post-interview questionnaires. Through thematic analysis, we identified seven design considerations while designing TUIs to interact with algorithmic movie recommender systems: (1) Distinctions between TUIs and GUIs; (2) TUIs replacing predominant interfaces; (3) Preference for single-device TUIs; (4) The relevance of granular control for TUIs; (5) Apparent transparency limitations of TUIs; (6) TUIs and algorithmic social computing; and (7) Overview of specific design choices, including advantages and disadvantages of soft, hard, rounded, cubic, and humanoid interfaces. These findings inspired Recffy: the first functional TUI designed to enhance awareness and control in personalized movie recommendations. Based on this study, we propose the concept of Tangible Algorithms: TUIs dedicated to enhancing the interaction of algorithmic systems and their profiling processes or decisions in a specific context. Furthermore, we describe the relevance of tangible algorithms and design guidelines to promote them in diverse AI contexts. Finally, we invite the HCI and CSCW community to continue exploring tangible algorithms to address the interaction with algorithmic systems, including the collaborative and social computing dynamics they can promote in diverse AI contexts.

Questionnaires are vital in games user research (GUR) to assess player experience (PX). However, having too many questions in surveys prevents wider uptake among GUR professionals because of games' rapid production cycles. To address this issue, we present the miniPXI---an eleven-item measure of the popular Player Experience Inventory (PXI)---providing single items for each of its eleven constructs. To develop the scale and examine its reliability and validity, we present three studies, conducted with 15 experts and 628 digital game players across continents. In the first survey study (n=366, 15 experts), single items were selected. In a second survey study (n=232), we explored reliability and validity of the single-item scale. Participants completed both full and single-item (SI) variants in three days. In the last study (n=30), we established the validity and sensitivity via an experimental evaluation of two games. The results are nuanced; SI reliability estimates for PXI constructs range from .51 to .83 with an average of .68, we could confirm the validity for nine constructs. We conclude that the miniPXI can be a valuable tool for PX evaluations where a longer measure is not feasible, and provide practical considerations for its use.

Recommender algorithms play an active role in many everyday activities. However, personalized recommendations often produce negative experiences due to a lack of awareness, control, or transparency. Allowing users to materialize their algorithmic imaginaries exposes how they experience, perceive, and imagine recommender algorithms. Moreover, it can unearth novel and previously unattended design opportunities for tangible interactions with algorithms. Therefore, we explored how 15 users of a famous movie recommender system materialized tangible designs to reflect and discuss their algorithmic imaginaries during co-design workshops and interviews. Using thematic analysis, we identified two forms of algorithmic imaginaries that can inspire tangible interactions with recommender algorithms: metaphoric and datafied representations. Complementary themes exposed the influence of contextual factors and diverse negative attitudes towards personalized movie recommendations. Based on these findings, we suggest design opportunities and suggestions for improving the algorithmic experience of movie recommendations and similar systems through tangible user interfaces.

Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it. We first look into infovis from the humanistic perspective by exploring the humanistic literature around infovis. We validate and expand those findings though a co-design workshop with humanist and infovis experts. Then, we translate our findings into guidelines for designers and conduct a design critique exercise to explore their effect on the perception of humanist researchers. Based on these steps, we introduce Layers of Meaning, a framework to reduce the semantic distance between humanist researchers and visualizations of their research material, by grounding infovis tools in time and space, physicality, terminology, nuance, and provenance.

sponsorship: EIT Health|16013

Physical activity (PA) positively impacts the quality of life of older adults, with technology as a promising factor in maintaining motivation. Within Computer Science and Engineering, research investigates how to track PA of older adults for various purposes. We present a systematic review of 204 papers and discuss wearable tracking systems according to their purpose, technological context, and target audience, as well as design and evaluation processes with particular attention to the meaningful involvement of older adults. Our results show that most systems focus on supervising older adults in the context of disease and frailty management. Only few systems focus on supporting older adults by promoting rehabilitation and respecting agency of older adults via self-monitoring PA, or encouraging PA to maintain healthy levels of activity. Moreover, systems are often narrowly limited to walking, although older adults may enjoy a broader range of activities. Likewise, the involvement of older adults in design processes is scarce, and their experience with a given technology is rarely considered relevant for evaluation. In sum, we contribute an overview of wearable technology for tracking older adults’ PA, contextualize our findings within recommendations provided by Sports and Rehabilitation Science, and illustrate opportunities for future work.

Games User Research (GUR) focuses on measuring, analysing and understanding player experiences to optimise game designs. Hence, GUR experts aim to understand how specific game design choices are experienced by players, and how these lead to specific emotional responses. An instrument, providing such actionable insight into player experience, specifically designed by and for GUR was thus far lacking. To address this gap, the Player Experience Inventory (PXI) was developed, drawing on Means-End theory and measuring player experience both at the level of Functional Consequences, (i.e., the immediate experiences as a direct result of game design choices, such as audiovisual appeal or ease-of-control) and at the level of Psychosocial Consequences, (i.e., the second-order emotional experiences, such as immersion or mastery). Initial construct and item development was conducted in two iterations with 64 GUR experts. Next, the scale was validated and evaluated over five studies and populations, totalling 529 participants. Results support the theorized structure of the scale and provide evidence for both discriminant and convergent validity. Results also show that the scale performs well over different sample sizes and studies, supporting configural invariance. Hence, the PXI provides a reliable and theoretically sound tool for researchers to measure player experience and investigate how game design choices are linked to emotional responses.

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.

Recently, a shift from curative to preventive care is promoted, by which patients are expected to become active and use diverse forms of self-management health systems (SMHS), i.e., integrated solutions that present data from multiple sensors and/or self-reports, possibly enhanced with risk assessment and decision support, to perform health-related actions. This is promoted as contributing to living longer and healthier, assisting ageing-in-place. Hence, older adults have also become a potential target of SMHS. While studies are performed on uses and attitudes of specific patient groups towards SMHS, studies that focus on older, including the oldest, adults are scarce. Therefore, we report on a qualitative study with 20 older adults (mean age = 80). Through thematic analysis, we identified four themes (i.e., enforced use of technology; need for support in technology use; equivocal stance towards sharing data; hypothetical value of technology for healthy ageing) to provide a deeper understanding of older adults' attitudes and engagement with information- and communication technologies (ICT) in general and SMHS in particular. We also present four pitfalls, unified by a central concept "Ageing is not a disease'', along with design considerations for future SMHS.

Squeeze interaction, defined as squeezing a soft object that affects computed feedback, is a promising interaction technique due to its expressive character. Skweezee for Processing is a Processing library that allows makers to implement squeeze interactions in a lightweight manner. The library offers a set of features based on data extraction algorithms and aims to preserve the dynamics of squeezes in the mapping from user action to system output. Distributing Skweezee for Processing as an open source library, we invite the community to further investigate the potential of squeeze interactions and to contribute to the extension and improvement of the library. We emphasize the potential for rich squeeze interaction by demonstrating a game that implements squeeze interaction as core mechanic. Additionally, we demonstrate the ease of implementing squeeze interactions in a variety of settings using the Skweezee for Processing library.

In this paper we explore whether Social Interdependence Theory (SIT) is a useful theoretical framework to anticipate on challenging intragroup dynamics in co-design with children. According to SIT, there are five principles that mediate the effectiveness of cooperation: positive interdependence, individual accountability, promotive interaction patterns, social skills and group processing. First, we theoretically ground six challenging group dynamics encountered in a previous study. Next, we introduce SIT and describe how we applied each of the five mediating principles in a new case study in which 49 children aged 9 to 10 were involved in a series of co-design sessions. Afterwards, we present our findings and reflect upon the SIT inspired co-design procedure. Finally we touch upon topics for further research and we make a call for more research on SIT in the Child Computer Interaction (CCI) community.

We present an evaluation of a DIY-toolkit, designed to empower caregivers to create tailor-made, unique assistive solutions for their clients. More specifically, the toolkit aims to enable occupational therapists to turn everyday soft objects into smart devices that can be programmed to recognize certain manipulations. These smart objects can then be used to control applications or to play certain games. Our evaluation reveals that occupational therapists were able to make use of the toolkit without the aid of a technical expert. The therapists hacked everyday objects such as cushions, socks, cuddly toys and repurposed them for therapy. They computationally augmented them and tailored them to clients' needs and desires. From our evaluation, we also derive five guidelines that can inform others when creating DIY-toolkits for assistive technology.

This paper presents a structured way to evaluate challenging group or 'codesign dynamics' in participatory design processes with children. In the form of a critical reflection on a project in which 103 children were involved as design partners, we describe the most prevalent codesign dynamics. For example, some groups rush too quickly towards consensus to safeguard group cohesiveness instead of examining other choice alternatives (i.e., groupthink). Besides 'groupthink' we describe five more challenging codesign dynamics: 'laughing out loud', 'free riding', 'unequal power', 'apart together' and 'destructive conflict'. We argue that balancing these dynamics has a positive impact on the dialectic process of developing values and ideas in participatory design, as well as on children's motivation. Therefore, the CCI community could benefit from our indepth exploration and categorization of challenging group dynamics when codesigning technology with children.

Skweezees are soft, deformable objects that recognize their shape deformation. Typically, a Skweezee has a fabric 'skin' and is filled with conductive padding. Several fabric electrodes are dispersed over the shape, and a small electronic circuit measures the resistance between each possible pair of electrodes. As the shape is deformed as a result of a squeeze gesture, the resistance patterns between electrode pairs change. A machine learning algorithm allows to differentiate between the different shape deformations. In addition, user-friendly open source software allows defining and recording squeeze gestures. Consequently, Skweezees enable rich gestural squeeze interaction for the DIY community. In this Skweezee Studio, participants are invited to bring their own plush toy (or another soft object) and to transform it into a Skweezee. Moreover, participants will be introduced to the mechanical, electrical and digital properties of Skweezees and participants will be able to explore and discuss the potential of e-textile, and of soft, tangible and haptic interactions in general.

Over the past decades, preschoolers emerged as frequent and experienced users of new digital media. As this trend continues, it is important for game designers to address the gratifications of this new gaming audience. Unfortunately, existing theoretical frameworks on meaningful gameplay for preschoolers are rare, outdated, or they lack a comprehensive validation. In this paper, we present a User Experience (UX) Laddering study to unveil the gameplay preferences of preschoolers, relying on five-year olds (n=25) as active research participants. The results of this study provide a set of meaningful and useful guidelines for future game designers, directed at this young target group.

The Skweezee System is an easy, flexible and open system for designing and developing squeeze-based, gestural interactions. It consists of Skweezees, which are soft objects, filled with conductive padding, that can be deformed or squeezed by applying pressure. These objects contain a number of electrodes that are dispersed over the shape. The electrodes sense the shape shifting of the conductive filling by measuring the changing resistance between every possible pair of electrodes. In addition, the Skweezee System contains user-friendly software that allows end-users to define and to record their own squeeze gestures. These gestures are distinguished using a Support Vector Machine (SVM) classifier. In this paper we introduce the concept and the underlying technology of the Skweezee System and we demonstrate the robustness of the SVM based classifier via two experimental user studies. The results of these studies demonstrate accuracies of 81% (8 gestures, user-defined) to 97% (3 gestures, user-defined), with an accuracy of 90% for 7 pre-defined gestures.

In this paper we extend the field of organic user interfaces and introduce the Splash Controller. The main concept of a Splash Controller is that a user interacts with computing technology by manipulation of water in some kind of receptacle. To this end we highlight the possibilities of Splash Controllers, specifically as game controllers. Next, we specify a simple and robust technology for the detection of water. In order to demonstrate the feasibility of a Splash Controller, we additionally present the design and development of one specific Splash Controller prototype.

With the advent of computer games involving the movement of the player's whole body or body parts, an opportunity arises to develop games for people with motor disabilities. In this paper we present four minigames developed for people suffering from spasticity and loss of motor control. We thereby focus on the input devices, sensor signal processing and mapping of players' actions on events in the game. In order to adapt the game to the player's motor skills and goals, specific attention should be paid to calibration procedures and adjustable parameters. We illustrate how this can be done and simultaneously, we demonstrate the feasibility for the development of digital games for physical therapy with currently available commercial input devices.

In this paper, we investigate the usefulness and feasibility of Laddering with young children in User eXperience evaluations. We start with a revision of theoretical literature and guidelines. Developmental literature suggests that children aged two to seven years old have the cognitive capabilities to perform as Laddering interviewees. Next, we put these findings to the test via a practical case. The results of our case study demonstrate that only the older children, aged five years and older, were able to construct meaningful ladders. As for the type of ladders created, our results are in line with literature; children are inclined to attribute external reasons to product preferences rather than internal reasons, and consequently create ladders of attributes and consequences, not reaching for values.

This paper discusses the quest for a framework that helps to research and design technology that supports fun activities of preschoolers in the home environment. We therefore review literature within Child-Computer Interaction and the Uses & Gratifications framework in Mass Communication Sciences. We propose that in order to be likeable, a product should fulfill the gratifications of the user. We conceptualize interactive toys and games no longer as tools but as media. Through the use of probes and observations, we adapt and validate our framework to arrive at a final classification of gratifications. Our framework enumerates five important gratifications of preschoolers: (1) challenge & control, (2) social experiences, (3) fantasy, (4) creative & constructive expressions, and (5) body & senses. Furthermore, we detail how basic needs, individual characteristics and societal aspects complement the Likeability Framework.