This paper aims to analyse the state-of-the-art of somaesthetics, describing the scientific and philosophic basis of the discipline, in order to devise how to implement a soma-based design. The goal is to apply the somaesthetics approach to the design of multisensory interactive systems for the purpose of creating novel technology designs for people with disabilities that can foster their participation and improve their daily life and overall well-being. Somatics can be intended as a set of instrumental values to increase bodily awareness. It allows us to get in touch with our own inner states, which can lead to a personal evaluative dimension for the designer, which can be used to integrate existing methods for evaluating experiences. Paying attention to one's own bodily states is key as it can turn such states in desing material. This concept was further developed with Shusterman's somaesthetics, a theoretical framework for aesthetic experiences. In the paper, we present examples of somaesthetics approaches to the design of interfaces (e.g. the SomaMat, that uses heat stimuli to guide the user attention to different parts of his/her body and can be a support for exercising or a tool to increase the body awareness). Moreover, we illustrate existing practices to design such as Embodied Sketching or Moving and Making Strange that are based on the body as the starting point of the design process, usually preceded by techniques such as defamiliarization or Feldenkrais exercises. Then, we move on to our forthcoming research, aimed at applying a somaesthetics approach to create a system for two possible groups of users: (1) children who are patients at the Giannina Gaslini Institute (a pediatric hospital) and (2) visually impaired and blind people members of Unione Italiana Ciechi (an Italian association of blind people). The system will help users move together, to increase interaction between them as well as involve them in educational and creative activities. The work will be carried out under the PNRR RAISE project (Robotics and AI for Socio-economic Empowerment; https://www.raiseliguria.it/ ).
In sports games, the excitement and suspense felt by the spectators are essential to their entertainment experience. The level of excitement and suspense is linked to the spectators' reasoning about the probability of winning or losing. In tennis, as in many other sports, spectators' predictions of winning probabilities largely hinge on the scores. Given tennis's hierarchical scoring system, its probabilistic reasoning is multifaceted and complex. This research examines the winning probabilities across various scoring scenarios, using data from thousands of professional tennis matches and comparing them with theoretical models generally aligned with spectators' common beliefs. The analysis reveals that the theoretical model makes accurate probability predictions at the macro level but inaccurate predictions at the micro level, pointing to possible biases in micro-level probabilistic reasoning. A recent behavioral economic theory may help explain the causes of such biases. Biases are generally seen as undesirable errors, but this study offers a counterargument that biases in micro-level probabilistic reasoning actually enhance the enjoyment of tennis matches by creating expectations, anxiety, and surprises.
This paper develops the strategic quotient construct and its measure, the Strategic Quotient (SQ), a test to assess actual strategic competencies. While the developmental and refinement work is still in progress, here we document the process of rethinking the foundations of the test and the concepts it is based on, in order to design a novel version of the SQ, the "multiple choice Strategic Quotient" (mcSQ).
This paper offers an easy introduction of Video Game Artificial intelligence (VGAI), i.e. the set of computational techniques embedded inside Non-Playable Characters (NPCs) that populate a video game and simulates the idea of playing against rational individuals. The two main pillars of VGAI will be briefly presented, namely pathfinding and decision-making, alongside an introduction and an explanation of the difference between academic AI research and video game AI. In summary, this paper delivers a solid but easy-to-understand introductory guide to this interesting and long-running research and industrial field without sacrificing mathematical formalism but not going into every detail, as the most industry-used books and academic references will be provided.
In the field of football analytics, the goal is to improve (in terms of prediction performance) one of the emerging tools: the expected goal (xG) model. With this final aim, data from different sources have been merged: tracking data, match event data and some players' performance composite indicators obtained using a Partial Least Squares - Structural Equation Model (PLS-SEM) approach. Using a sample of match data relying to season 2019/2020 of the Italian Serie A, composed by 1 outcome variable (i.e. the GOAL) and 22 features, a logistic regression model was applied on different scenarios for sample balanced techniques. Results seem to be interesting in terms of sensitivity, F1 and AUC metrics, compared with a benchmark. In addition, some original performance composites and tracking variables introduced are significant for the classification model.
