Sports constitute a basis of fun for many (young) people and can contribute to important social developments. Sport can also yield opposite results:: arrogance, bullying, (sexual) intimidation, discrimination. Effects are largely influenced by the context of (youth) sport participation. In facing the reality of organized sport, creating a safe and pedagogical environment is not a priori inherent in sports. Many sports associations and clubs still primarily and too often focus on issues like schedules, organizing training and competitions, being financially sound and ensuring the continuity of their organizations. Specific policies for the prevention of harassment and abuse in sport hardly ever reach a local level. Experts speak of an implementation issue. In search for innovative approaches, Positive Behavior Support (PBS) was also introduced in the sport context. In this article, the preliminary evaluation of first experiences with PBS in local sport clubs (which are mostly run by volunteers) is described on the basis of seven insights. Conclusions can be drawn that PBS can be a guiding principal in sport clubs with organizational strength, although it needs specific translation within the context of sport. Further research is required to determine those significant adjustments.
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.
The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
Digital innovations in the field of immersive Augmented Reality (AR) can be a solution to offer adults who are mentally, physically or financially unable to attend sporting events such as premier league football a stadium and match experience. This allows them to continue to connect with their social networks. In the intended project, AR content will be further developed with the aim of evoking the stadium experience of home matches as much as possible. The extent to which AR enriches the experience is then tested in an experiment, in which the experience of a football match with and without AR enrichment is measured in a stadium setting and in a home setting. The experience is measured with physiological signals. In addition, a subjective experience measure is also being developed and benchmarked (the experience impact score). Societal issueInclusion and health: The joint experience of (top) sports competitions forms a platform for vulnerable adults, with a limited social capital, to build up and maintain the social networks that are so necessary for them. AR to fight against social isolation and loneliness.
