Deaf and hard-of-hearing (DHH) individuals using cochlear implants (CIs) often have regular jobs or enroll in mainstream education where they face complex social challenges. While first HCI interventions targeted this group's communication skills, or compensated for limited sound perception, we instead focused on experiential aspects like fatigue and feeling different from others. We moved beyond individual-focused design by engaging interaction-partners to share responsibility for overcoming social barriers. This work identifies generative, intermediate-level design knowledge, addressing common interaction-level challenges. A design-oriented, thematic analysis of interviews with 14 CI users revealed four subsequent themes: invisible, shifting hearing demands; misunderstandings and social impact; strategies for managing interaction barriers; and emotional, relational costs. Mapping these themes to HCI concepts like seamfulness, social translucence, and proxemics highlights open-ended, concrete design opportunities that support socializing beyond functional access. Framing interaction success as shared responsibility broadens inclusive design discourse for DHH populations and wider disability design spaces.

Abstract In this paper, we use self-determination theory and its related mini-theories to investigate the influence of sport data on sports experience and motivation in sports. First, we reflect on the use of technology in sports and show how sport data thwarts and promotes motivation in sports. Second, we argue that human–computer interaction (HCI) has been too narrowly focused on the ‘performance’ aspect of sport data. We argue for a more liberal take on sport data, showing that it also relates to motivation in sports through basic human needs. By bridging SportsHCI studies with the insights we gain from self-determination theory, we uncover the interwoven relations between the objective measures that sports technology provides and their motivational aspects for athletes. Our paper ends with five emerging points for attention for SportsHCI that we think can pave the way towards a more holistic approach to considering sport data for motivation in sports.

Abstract Movement-based design methods have gained increased attention across various research fields and practices, particularly in interaction design. By engaging the body in movement, these methods have the potential to explore a richer, more intuitive, and immersive user experience. A recent project MeCaMinD1 brought together researchers from interaction and sports design to explore, collect, and document movement-based methods and make them practically useful across domains. The methods were explored through a series of workshops, furthering the team’s understanding of their use and practical applicability. This understanding was compiled into a set of design cards that we present here. We discuss the experience of using the resulting cards in movement-based design sessions. We found that while the cards are mostly used in designing, planning, and preparing sessions, they also function as support during the design sessions, used by both facilitators and participants. Based on a final ideation session with both novice and experienced facilitators, we sketch ways to support managing the cards during sessions, integrating them with movement and physical action.

The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.

Movement-based design methods are increasingly adopted to help design rich embodied experiences. While there are well-known methods in the field, there is no systematic overview to help designers choose among them, adapt them, or create their own. We collected 41 methods used by movement design researchers and employed a practice-based, bottom-up approach to analyze and characterize their properties. We found 17 categories and arranged them into five main groups: Design Resources, Activities, Delivery, Framing, and Context. In this paper, we describe these groups in general and then focus on Design Resources containing the categories of Movement, Space, and Objects. We ground the characterization with examples from empirical material provided by the design researchers and references to previous work. Additionally, we share recommendations and action points to bring these into practice. This work can help novice and seasoned design researchers who want to employ movement-based design methods in their practice.

In Human Behaviour Understanding, social interaction is often modeled on the basis of lower level action recognition. The accuracy of this recognition has an impact on the system's capability to detect the higher level social events, and thus on the usefulness of the resulting system. We model team interactions in volleyball and investigate, through simulation of typical error patterns, how one can consider the required quality (in accuracy and in allowable types of errors) of the underlying action recognition for automated volleyball monitoring. Our proposed approach simulates different patterns of errors, grounded in related work in volleyball action recognition, on top of a manually annotated ground truth to model their different impact on the interaction recognition. Our results show that this can provide a means to quantify the effect of different type of classification errors on the overall quality of the system. Our chosen volleyball use case, in the rising field of sports monitoring, also addresses specific team related challenges in such a system and how these can be visualized to grasp the interdependencies. In our use case the first layer of our system classifies actions of individual players and the second layer recognizes multiplayer exercises and complexes (i.e. sequences in rallies) to enhance training. The experiments performed for this study investigated how errors at the action recognition layer propagate and cause errors at the complexes layer. We discuss the strengths and weaknesses of the layered system to model volleyball rallies. We also give indications regarding what kind of errors are causing more problems and what choices can follow from them. In our given context we suggest that for recognition of non-Freeball actions (e.g. smash, block) it is more important to achieve a higher accuracy, which can be done at the cost of accuracy of classification of Freeball actions (which are mostly plays between team members and are more interchangable as to their role in the complexes).

Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multi-modal feedback.

Interactive playgrounds are spaces where players engage in collocated, playful activities, in which added digital technology can be designed to promote cognitive, social, and motor skills development. To promote such development, different strategies can be used to implement game mechanics that change player's in-game behavior. One of such strategies is enticing players to take action through incentives akin to game achievements. We explored if this strategy could be used to influence players' proxemic behavior in the Interactive Tag Playground, an installation that enhances the traditional game of tag. We placed the ITP in an art gallery, observed hundreds of play sessions, and refined the mechanics, which consisted in projecting collectible particles around the tagger that upon collection by runners resulted only in the embellishment of their circles. We implemented the refined mechanics in a study with 48 children. The playground automatically collected the players' positions, and analyses show that runners got closer to and moved more towards taggers when using our enticing strategy. This suggests an enticing strategy can be used to influence physical in-game behavior.

We are working towards a socially adaptive digital playground for children. To this end, we are looking into nonverbal synchrony and other social signals as a measure of social behaviour and into ways to alter game dynamics to trigger and inhibit certain social behaviours. Our first results indicate that we can indeed influence social behaviours in a digital playground by changing game dynamics. Furthermore, our first results show that we will be able to sense some of these social behaviours using only computer vision techniques. I propose an iterative method for working towards a socially adaptive digital playground.