"There is more that unites us than divides us". Optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities.

Sports are characterized by unique rules, environments, and tasks, but also share fundamental similarities with each other sport. Such between-sports parallels can be vital for optimizing talent transfer processes. This study aimed to explore similarities between sports to provide an objective basis for clustering sports into families by means of machine learning.

Fully Automatic Camera for Personalized Highlight Generation in Sporting Events.

Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data.

A machine learning approach for the classification of sports based on a coaches' perspective of environmental, individual and task requirements: A sports profile analysis.

Well-designed talent programmes in sports with a focus on talent identification, orientation, development, and transfer support the engagement of young individuals and the pursuit of elite performance. To facilitate these processes, an analysis of task, environmental and individual characteristics per sport is much needed. The aims of this study were to 1) analyse whether unique profiles per sport could be established by generic characteristics and 2) to discuss similarities and differences for the potential application in talent development and transfer.

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings.

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations.

Association between SARS-COV-2 infection and muscle strain injury occurrence in elite male football players: a prospective study of 29 weeks including three teams from the Belgian professional football league.

Objectives: The aim of this study was to investigate the association between SARS-CoV-2 infection and muscle strain injury in elite athletes.

Methods: A prospective cohort study in three Belgian professional male football teams was performed during the first half of the 2020-2021 season (June 2020-January 2021). Injury data were collected using established surveillance methods.

Video-Based Analysis and Reporting of Riding Behavior in Cyclocross Segments.

Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.

A Machine Learning Approach to Assess Injury Risk in Elite Youth Football Players.

Purpose: To assess injury risk in elite-level youth football (soccer) players based on anthropometric, motor coordination and physical performance measures with a machine learning model.

Methods: A total of 734 players in the U10 to U15 age categories (mean age, 11.7 ± 1.