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.

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.

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.

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.