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.

As artificial intelligence (AI) becomes more embedded in personal health technology, its potential to transform health decision-making through personalised recommendations is becoming significant. However, there is limited understanding of how individuals perceive AI-assisted decision-making in the context of personal health. This study investigates the impact of AI-assisted decision-making on trust in physical activity-related health decisions. By employing MoveAI, a GPT-4.0-based physical activity decision-making tool, we conducted a mixed-methods study and conducted an online survey (N=184) and semi-structured interviews (N=24) to explore this dynamic. Our findings emphasise the role of nuanced personal health recommendations and individual decision-making styles in shaping trust in AI-assisted personal health decision-making. This paper contributes to the HCI literature by elucidating the relationship between decision-making styles and trust in the AI-assisted personal health decision-making process and showing the challenges of aligning AI recommendations with individual decision-making preferences.

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.

Training Load Management (TLM) is crucial for achieving optimal athletic performance and preventing chronic sports injuries. Current sports trackers provide runners with data to manage their training load. However, little is known about the extent and the way sports trackers are used for TLM. We conducted a survey (N=249) and interviews (N=24) with runners to understand sports tracker use in TLM practices. We found that runners possess some understanding of training load and generally trust their trackers to provide accurate training load-related data. Still, they hesitate to strictly follow trackers’ suggestions in managing their training load, often relying on their intuitions and body signals to determine and adapt training plans. Our findings contribute to SportsHCI research by shedding light on how sports trackers are incorporated into TLM practices and providing implications for developing trackers that better support runners in managing their training load.

Technology allows runners to obtain information on their running parameters and gain insights to self-reflect on their performance and technique. One of the higher levels of self reflection is transformative self-reflection, which engages runners in a thought process to adjust future efforts. Observing one-self in a video as well as visualizing running parameters have shown benefits in supporting self-reflection; combining the two thus presents an interesting research opportunity. This paper presents the process of designing a dashboard where abstract visuals are combined with drone video to promote runners’ self-reflection. A user centered approach was employed to select the parameters, design the visuals, and evaluate the dashboard. Evaluation using TSRI substantiated the dashboard’s capabilities to provide runners valuable insights and support their running data exploration. Interviews highlighted the dashboard’s potential in promoting self-reflection and provided design considerations for future iterations.

There is an underexplored interaction space for drones that can be utilised as running interaction technology, distinct from human drone interaction that warrants foregrounding. This paper consolidates the current state of art in running interaction technology through a review of relevant studies and commercial technologies in a framework positioned using dimensions related to the form of interaction as identified in the sports ITECH framework. Our analysis highlights the unmet opportunities in running interaction technology and presents the potential of drones to further support runners. The potential of drones to support various forms of interaction are supported using exemplar research done in human-drone interaction. Through our findings, we hope to inform and expedite future research and practice in the field of running interaction technology and runner drone interaction by supporting researchers in defining and situating their contributions.

Design practices targeting playful movement-based interaction are changing rapidly with both technological and societal developments. In this paper, we provide a snapshot of movement-based interaction designers’ experiences during the Covid-19 pandemic, by interviewing designers working in diverse roles that integrate physical human body movement and digital technologies (i.e., exergame and wearable game designers, playground and landscape architects, sports and dance trainers, and Sports-HCI professionals). Using grounded theory, we have identified two tactics from the designers’ experiences: (i) the significance of face-to-face embodied interactions throughout the entire movement-based interaction design process, and (ii) the importance of positive, yet critical, attitudes to technology for designing bodily experiences, including mixed use of non-technical materials and tools for rapid prototyping and iteration. However, it was evident that such tactics are often not feasible without physical interaction between the designer and users. The restrictions imposed by Covid-19, therefore, further revealed the importance of body movement to designing a playful movement-based interaction — from ideation to execution and testing. This paper offers a worrying view of movement-based interaction design during the Covid-19 era, while calling for further investigation to enrich the discourse that could contribute to this field, possibly also beyond the circumstances of the Covid-19 pandemic.

The global COVID-19 pandemic left few facets of our lives unchanged, including the possibility to play organised sports. Many gyms and sports clubs had to shut down due to social distancing measures, which resulted in reduced physical activity. Co-located group sports that promote social interaction and fun became almost impossible. This lack of physical interaction combined with social isolation and confinement have shown negative impacts on physical and mental well-being of people (for example, lack of motivation, anxiety, low immunity, low academic performance, negative mood and feelings) especially in children and young adults. Recent research therefore recommends exploring more creative approaches that allow physical activities during a pandemic, especially the ones that increase social interaction and engagement and allow users to share the same physical space. Towards this, we developed SixFeet, a novel digital-physical sports platform that allows its participants to play rigorous, collaborative sports – at a six feet distance. SixFeet ensures that the players are 1.5m away from each other at all times without them noticing it or without them having to worry about the social distancing measures. We developed a prototypical implementation of SixFeet and conducted a pilot study to evaluate its technical feasibility and practicality as well as obtain a first insight into user experience with the platform. Results show that participants never came within 1.5 meters of one another, felt connected nevertheless and were physically exerted. This paper presents the design and architecture of SixFeet, results of the preliminary user study, and a discussion on the versatility of SixFeet in accommodating training for different sports in times of a pandemic.

The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications.

Unpredictability in robot behaviour can cause difficulties in interacting with robots. However, for social interactions with robots, a degree of unpredictability in robot behaviour may be desirable for facilitating engagement and increasing the attribution of mental states to the robot. To generate a better conceptual understanding of predictability, we looked at two facets of predictability, namely, the ability to predict robot actions and the association of predictability as an attribute of the robot. We carried out a video human-robot interaction study where we manipulated whether participants could either see the cause of a robot’s responsive action or could not see this, because there was no cause, or because we obstructed the visual cues. Our results indicate that when the cause of the robot’s responsive actions was not visible, participants rated the robot as more unpredictable and less competent, compared to when it was visible. The relationship between seeing the cause of the responsive actions and the attribution of competence was partially mediated by the attribution of unpredictability to the robot. We argue that the effects of unpredictability may be mitigated when the robot identifies when a person may not be aware of what the robot wants to respond to and uses additional actions to make its response predictable.

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study.

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).

This article presents a study in which we explored the effect of a social robot on the explanatory behavior of children (aged 6--10) while working on an inquiry learning task. In a comparative experiment, we offered children either a baseline Computer Aided Learning (CAL) system or the same CAL system that was supplemented with a social robot to verbally explain their thoughts to. Results indicate that when children made observations in an inquiry learning context, the robot was better able to trigger elaborate explanatory behavior. First, this is shown by a longer duration of explanatory utterances by children who worked with the robot compared to the baseline CAL system. Second, a content analysis of the explanations indicated that children who worked with the robot included more relevant utterances about the task in their explanation. Third, the content analysis shows that children made more logical associations between relevant facets in their explanations when they explained to a robot compared to a baseline CAL system. These results show that social robots that are used as extensions to CAL systems may be beneficial for triggering explanatory behavior in children, which is associated with deeper learning.

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.

This paper presents progress and challenges in developing a platform for multi-character, argumentation based, interaction with a group of virtual coaches for healthcare advice and promotion of healthy behaviours. Several challenges arise in the development of such a platform, e.g., choosing the most effective way of utilising argumentation between the coaches with multiple perspectives, handling the presentation of these perspectives and finally, the personalisation and adaptation of the platform to the user types. In this paper, we present the three main challenges recognized, and show how we aim to address these.

We demonstrate here the first Technical Demonstrator for Council of Coaches a project that is aiming to deploy a platform for virtual health coaching, and will incorporate computational models of argument and dialogue.

We present anew dialogue engine called Flipper 2.0 (Flipper) which aims to help developers of embodied conversational agents (ECAs) to quickly and flexibly create dialogues. Flipper provides a technically stable and robust dialogue management system to integrate with other components of ECAs such as behaviour realisers. We compare Flipper with state-of-the-art dialogue design systems. We describe the details of our dialogue engine, how it handles dialogue management and how it supports the authoring of dialogues. We demonstrate the use of the dialogue engine with examples of design patterns and discuss practical applications. Finally we give recommendations on the cases in which it is beneficial to use Flipper.

Remote mixed reality (MR) collaboration systems allow for multimodal, real-time support from remote experts. We present our open toolkit that provides a flexible end-to-end solution for building such systems using off-the-shelf hardware. From related work, three core design aspects have been identified: 1) the independence of the viewpoint that the visitor (the remote expert) can take in relation to position and viewpoint of the visitee, 2) the immersiveness of the presentation technology for visitor and visitee, and 3) the extent to which the visitor's body is represented in the visitee's environment. This paper describes the implementation of our system, which includes these aspects. In a study aimed at validating whether we implemented these core aspects to good effect, conducted with a collaborative puzzle application built with our toolkit, we examine how variations of these aspects contribute to usability, performance and social presence related metrics.

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.

This review examines recent methodological approaches for the evaluation of child-robot interaction in learning settings. The main aims are to map existing work from a user-centered perspective, to identify possible trends related to evaluation methods for child-robot interaction, and to discuss potential future directions. We present a systematic review of existing studies, which have been thematically organized based on their research objectives. We then examine the evaluation methods that were used in these studies and we propose a conceptual framework based on the one hand on the themes that emerged, namely the social interaction between the child and the robot, the social acceptance, possible emotional interactions, the learning process and the learning outcome, and on the other hand on the corresponding measures. These methods have been considered in relation with the age ranges of the children, because of the relationship of their cognitive level to the choice of a developmentally appropriate evaluation method. We use this framework to highlight current trends and needs for the field and to contextualize the methodological directions for child-robot interaction. Finally, we discuss the challenges and limitations of the current methodological approaches as well as possible future directions for the evaluation methods of child-robot interaction in learning settings.

Nonverbal synchrony is an important and natural element in human-human interaction. It can also play various roles in human-computer interaction. In particular this is the case in the interaction between humans and the virtual humans that inhabit our cyber worlds. Virtual humans need to adapt their behavior to the behavior of their human interaction partners in order to maintain a natural and continuous interaction synchronization. This paper surveys approaches to modeling synchronization and applications where this modeling is important. Apart from presenting this framework, we also present a quantitative method for measuring the level of nonverbal synchrony in an interaction and observations on future research that allows embedding such methods in models of interaction behavior of virtual humans.

The Virtual Conductor is an artificial conducting system for tutoring purposes that uses real-time audio analysis of music played by musicians and uses this analysis to animate a virtual human that acts as a conductor. The analysis detects the tempo and the dynamics of the music, compares the results with the score and the intentions of the conductor and modifies the conducting behavior when the musicians deviate from the desired performance.

In computational linguistics, a reliability measurement of 0.8 on some statistic such as κ is widely thought to guarantee that hand-coded data is fit for purpose, with 0.67 to 0.8 tolerable, and lower values suspect. We demonstrate that the main use of such data, machine learning, can tolerate data with low reliability as long as any disagreement among human coders looks like random noise. When the disagreement introduces patterns, however, the machine learner can pick these up just like it picks up the real patterns in the data, making the performance figures look better than they really are. For the range of reliability measures that the field currently accepts, disagreement can appreciably inflate performance figures, and even a measure of 0.8 does not guarantee that what looks like good performance really is. Although this is a commonsense result, it has implications for how we work. At the very least, computational linguists should look for any patterns in the disagreement among coders and assess what impact they will have.

In this paper we raise the issue whether imperfections, characteristic of human-human communication, should be taken into account when developing virtual humans. We argue that endowing virtual humans with the imperfections of humans can help making them more 'comfortable' to interact with. That is, the natural communication of a virtual human should not be restricted to multimodal utterances that are always perfect, both in the sense of form and of content. We illustrate our views with examples from two own applications that we have worked on: the Virtual Dancer, and the Virtual Trainer. In both applications imperfectness helps in keeping the interaction engaging and entertaining.