Enhancing volleyball training:empowering athletes and coaches through advanced sensing and analysis

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.</p

Towards Automatic Modelling of Volleyball Players' Behavior for Analysis, Feedback and Hybrid Training

Automatic tagging of video recordings of sports matches and training sessions can be helpful to coaches and players and provide access to structured data at a scale that would be unfeasible if one were to rely on manual tagging. Recognition of different actions forms an essential part of sports video tagging. In this paper, the authors employ machine learning techniques to automatically recognize specific types of volleyball actions (i.e., underhand serve, overhead pass, serve, forearm pass, one hand pass, smash, and block which are manually annotated) during matches and training sessions (uncontrolled, in the wild data) based on motion data captured by inertial measurement unit sensors strapped on the wrists of eight female volleyball players. Analysis of the results suggests that all sensors in the inertial measurement unit (i.e., magnetometer, accelerometer, barometer, and gyroscope) contribute unique information in the classification of volleyball actions types. The authors demonstrate that while the accelerometer feature set provides better results than other sensors, overall (i.e., gyroscope, magnetometer, and barometer) feature fusion of the accelerometer, magnetometer, and gyroscope provides the bests results (unweighted average recall = 67.87%, unweighted average precision = 68.68%, and κ = .727), well above the chance level of 14.28%. Interestingly, it is also demonstrated that the dominant hand (unweighted average recall = 61.45%, unweighted average precision = 65.41%, and κ = .652) provides better results than the nondominant (unweighted average recall = 45.56%, unweighted average precision = 55.45, and κ = .553) hand. Apart from machine learning models, this paper also discusses a modular architecture for a system to automatically supplement video recording by detecting events of interests in volleyball matches and training sessions and to provide tailored and interactive multimodal feedback by utilizing an HTML5/JavaScript application. A proof of concept prototype developed based on this architecture is also described

“Dear IOC” Considerations for the Governance, Valuation, and Evaluation of Trends and Developments in eSports

In 2021, the International Olympic Committee ventured virtual space by launching their first ever Olympic Virtual Series – featuring virtual baseball, cycling, rowing, sailing and motor racing. Interestingly, all these virtual events take strongly after their physical counterparts. Which begs the question: Where are the massively popular esports games like Fortnite, League of Legends, and Dota?–What do the Olympic Virtual Series have that these popular video games do not? Here, we argue for the inclusion of esports within the Olympic program. In many respects, esports “act” and “behave” just like traditional sports. We argue that esports and traditional sports share many of the same values, like the values of meritocracy, competition, fair play, and the value of having a “level playing field”. Yet, in esports, many of these values remain underappreciated, losing out to negative values such as physical inactivity and game-addiction. To preserve what is worth preserving, we borrow from Value Sensitive Design to ameliorate the design-tensions that are foregrounded in esports. Thereby, paving possible ways toward the inclusion of esports in the Olympic program. Ultimately, the question for the IOC should not be “does it look like ‘real sport’, as we know it?”, but rather: are they sporting, rule-led, and fair activities worth preserving and setting an example for a new digitally savvy generation

A Design Space of Sports Interaction Technology

With this monograph we introduce a new, systematic taxonomy of Sports Interaction Technology (Sports ITech) that defines a design space of existing and future work in this domain. We set the taxonomy in a context of our view on sport science and sports practice, target outcomes of sports and the underlying factors influencing them, and the role that sports technology plays to support sports science and practice. In that setting we systematically build and illustrate a taxonomy for the design space for Sports ITech as a sub-area of sports technologies, with specific attention for the adequate inclusion of knowledge from the sports sciences. We build on the basis of existing taxonomies and a vast body of literature from multiple domains of HCI, technology, sports science, and related work in Sports ITech, complemented with what we identified as obvious gaps in the literature. We finally share the conclusions after a discussion of the limitations of our work. The contributions of this monograph are as follows. First, we offer a description of a design space, exemplified through existing work in a way suitable to support designers, technologists, and sports people with a design mindset to design, deploy, and adapt Sports ITech. Second, we see this as a call to action to bring HCI and the sports sciences closer together in the new field of Sports Interaction Technology, to set a shared agenda for future developments. Third, we offer this as the collation of a reading guide and wayfinding support in the literature from the many underlying disciplines of Sports Interaction Technology

MeCaMInD.eu - Movement-Based Method Collection

The Method Cards for Movement-based Interaction Design (MeCaMInD) project is an ambitious initiative funded by the EU through the Erasmus+ strategic partnership program. Our primary objective is to introduce movement as an integral component in the design process of new practices, artifacts, and interaction designs for sports and physical activities. The intent is to drive the development of more sustainable movement technologies and sports concepts, enhancing health and well-being for people across Europe.MeCaMInD is a cooperative endeavour involving six prestigious institutions: the University of Southern Denmark, Aalto University, Malmö University, Universidad Carlos III de Madrid, the University of Twente, and Uppsala University. We are committed to creating and enhancing a movement-based creative design environment. The project caters to students and design professionals, providing an array of tools and methods.The movement-focused design approach aims to rethink how technology can be integrated more naturally and engagingly into our daily lives and activities. MeCaMInD envisions a future where technology, sports, and movement fuse seamlessly to promote an active, healthy, and joyful lifestyle