<abstract xmlns="http://www.w3.org/1999/xhtml">Robot-assisted training can be enhanced by using augmented feedback to support trainees during learning. Efficacy of augmented feedback is assumed to be dependent on the trainee's skill level and task characteristics. Thus, selecting the most efficient augmented feedback for individual subjects over the course of training is challenging.We present a general concept to automate feedback selection based on predicted performance improvement. As proof of concept, we applied our concept to trunkarm rowing. Using existing data, the assumption that improvement is skill level dependent was verified and a predictive linear mixed model was obtained. We used this model to automatically select feedback for new trainees. The observed improvements were used to adapt the prediction model to the individual subject. The prediction model did not over-fit and generalized to new subjects with this adaptation.Mainly, feedback was selected that showed the highest baseline to retention learning in previous studies. By this replication of our former best results we demonstrate that a simple decision rule based on improvement prediction has the potential to reasonably select feedback, or to provide a comprehensible suggestion to a human supervisor. To our knowledge, this is the first time an automated feedback selection has been realized in motor learning.</abstract>

Spinal Cord Injury Center, University Hospital Balgrist

Automated Feedback Selection for Robot-Assisted Training

<abstract xmlns="http://www.w3.org/1999/xhtml">Recently, a novel optical player tracking system has been developed to determine positional information of athletes in a non-invasive manner. The aim of this study was to measure the accuracy and reliability of displacement estimates derived from the system. Participants completed five soccer-specific running courses at three different speeds three times each, while being filmed using the multi-camera system. The participant’s <italic>x</italic>,<italic>y</italic> field positions were determined by the optical player tracking system and displacement was estimated using Euclidean distance, and compared with real distance. On average, the difference between actual distance and estimated displacement was 0.25% (mean absolute difference = 0.79 ± 0.56m) with a slightly larger coefficient of variation during 90° turns (4.89%) when compared with straight line running (4.09%). In addition, there were strong correlations between actual distance and measured displacement (<italic>r</italic> = 0.986 – 0.988). Collectively, the typical error (0.25 – 0.36 m), typical error as a coefficient of variation (1.06 – 1.75%) and intraclass correlation coefficient (0.88 – 0.93) showed high levels of intra-operator reliability. The optical player tracking system provides accurate and reliable estimates of displacement of players on a soccer field. This system provides non-invasive position detections for players and opposition players during soccer matches.</abstract>

Research Institute for Sport and Exercise

College of Science Health and Engineering

The Accuracy and Reliability of a New Optical Player Tracking System for Measuring Displacement of Soccer Players

<abstract xmlns="http://www.w3.org/1999/xhtml">Kvam and Sokol developed a successful logistic regression/Markov chain (LRMC) model for ranking college basketball teams part of Division I of the National Colligate Athletic Association (NCAA). In their 2006 publication, they illustrated that the LRMC model is one of the most successful ranking systems in predicting the outcome of the NCAA Division I Basketball Tournament. However, it cannot directly be extended to college football because of the lack of home-and-home matchups that LRMC exploits in performing its Logistic Regression. We present a common-opponents-based approach that allows us to perform a Logistic Regression and thus create a football LRMC (F-LRMC) model. This approach compares the margin of victory of home teams to their winning percentage in games played against common-opponents with the away team. Computational results show that F-LRMC is among the best of the many ranking systems tracked by Massey's College Football Ranking Composite.</abstract>

A Logistic Regression/Markov Chain Model for American College Football

<abstract xmlns="http://www.w3.org/1999/xhtml">The purpose of this study was twofold: 1) to discover the differences in degree of energy expenditure (EE) during Just Dance 2015 using Xbox 360 Kinect, Wii-U, PS3 Move, and Control YouTube video; and 2) to uncover whether or not exergaming could elicit moderate to vigorous levels of intensity (≥ 40% Heart Rate Reserve (HRR)) based on heart rate average (HRavg) measurements. Twenty-five healthy college-aged students participated in this study. Data collection was comprised of baseline testing, a 30 second familiarization period with each gaming console, and a gaming session. Participants danced to the song “Love Me Again” on a Just Dance 2015 program on Xbox 360 Kinect, Wii-U, PS3 Move, and a control YouTube. EE and HRR were calculated using FT4 Polar Heart Rate Monitor. One-way repeated measures ANOVA indicated no significant differences in energy expenditure across the consoles, <italic>F</italic>(2.74, 65.86)=0.65, <italic>p=</italic>.570. The paired samples t-test indicated the HRavg for the Xbox 360 Kinect (117±18 bpm) was significantly greater than the HRavg for the Control (112±16 bpm), <italic>t</italic>(24)=3.03, <italic>p</italic>=.006. About a third (28%-36%) of participants met moderate levels of intensity while exergaming. Dancing on all three major gaming consoles and YouTube video increase energy expenditures and can be used as an alternative form of exercise with the ability to achieve moderate levels of intensity.</abstract>

Comparisons of Heart Rate and Energy Expenditure During Exergaming in College-age Adults

<abstract xmlns="http://www.w3.org/1999/xhtml">The purpose of this study was to compare the competitive profiles across the Spanish football leagues at the present time. The final standings (n=32) and results of the matches played (n=11,122) in the 2015/2016 season were analysed. Four categories of analysis were selected: Level of competitive balance of matches, Level of compactability of team standings, Magnitude of home-field advantage effect, and Degree of openness of the matches. Using statistical procedures for the comparison of means by analysis of variance (ANOVA) and the Chi-Squared test, it was concluded that in the panorama of Spanish football, the men's 2nd division stands out as the Championship that corresponds to a competitive profile with greater equality and that the women's 1st division presents the most unbalanced competitive profile (p &lt; .05). A trend was also observed that indicated that the more professionalized Championships present a higher level of competitive balance of the matches, a higher level of compactability of the team standings, and a lower degree of openness of the matches with respect to the less professionalized Championships, due to the presence of statistically significant differences (p &lt; .05) in the set of categories analysed.</abstract>

Department of Physical and Sports Education

A comparison of competitive profiles across the Spanish football leagues

AHEAD OF PRINT

Volume 23 (2024): Issue 1 (February 2024)

Volume 22 (2023): Issue 2 (August 2023)

Volume 22 (2023): Issue 1 (March 2023)

Volume 21 (2022): Issue 2 (December 2022)

Volume 21 (2022): Issue 1 (March 2022)

Volume 20 (2021): Issue 2 (December 2021)

Volume 20 (2021): Issue 1 (July 2021)

Volume 19 (2020): Issue 2 (December 2020)

Volume 19 (2020): Issue 1 (July 2020)

Volume 18 (2019): Issue 3 (December 2019)

Volume 18 (2019): Issue 2 (September 2019) Special Issue: Selected papers presented at the 12th Symposium of the Section Computer Science in Sport of the German Association of Sport Science (September 4.-7., 2018)

Volume 18 (2019): Issue 1 (July 2019)

Volume 17 (2018): Issue 2 (December 2018)

Volume 17 (2018): Issue 1 (July 2018)

Volume 16 (2017): Issue 3 (December 2017)

Volume 16 (2017): Issue 2 (November 2017)

Volume 16 (2017): Issue 1 (July 2017)

Volume 15 (2016): Issue 2 (December 2016)

Volume 15 (2016): Issue 1 (July 2016)

International Journal of Computer Science in Sport

 The International Journal of Computer Science in Sport (IJCSS) is published in association with the International Association of Computer Science in Sport (IACSS).  It is a refereed electronic journal that is published biannually. Research results with an emphasis on the following topics regarding the application of Computer Science and Mathematics in supporting the development of theory and practice in sport are considered:   Modeling (mathematical, informatics, biomechanical, physiological)  Computer aided applications (software, hardware)  Data acquisition and processing  Data Bases  Simulation (interactive, animation etc.)  Development of theories  Multimedia / Internet  Presentation  Education   The journal does not only serve as a forum for current research from around the world in the field of computer technology in sport. Moreover, there is place to present theories, methods and concepts, which are developed in Computer Science and Mathematics, and applied to Sport Science.  Articles submitted to the IJCSS are reviewed by at least two anonymous referees. The reviewing process usually takes about a month. The authors are obliged to take the reviewers comments into account when preparing the final version of their article.  Why subscribe and read   Current research in all fields of Computer Science in Sport.  Forum for both established and young scholars.  No particular theories or trends favored.   Why submit   Fair and constructive double blind review.  Fast and efficient publication process.   Rejection Rate  &gt;30%  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 