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Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

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

Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

5 Articles
Open Access

Automated Feedback Selection for Robot-Assisted Training

Published Online: 29 Dec 2017
Page range: 149 - 174

Abstract

Abstract

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.

Keywords

  • virtual trainer
  • rowing simulator
  • motor learning
  • linear mixed models
  • statistical learning
Open Access

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

Published Online: 29 Dec 2017
Page range: 175 - 184

Abstract

Abstract

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 x,y 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 (r = 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.

Keywords

  • motion analysis
  • football
  • match analysis
  • performance analysis
Open Access

A Logistic Regression/Markov Chain Model for American College Football

Published Online: 29 Dec 2017
Page range: 185 - 196

Abstract

Abstract

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.

Keywords

  • logistic regresion
  • Markov chain
  • American college football
  • common game
  • margin of victory
Open Access

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

Published Online: 29 Dec 2017
Page range: 197 - 206

Abstract

Abstract

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, F(2.74, 65.86)=0.65, p=.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), t(24)=3.03, p=.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.

Keywords

  • exergames
  • energy expenditure
  • HR
Open Access

A comparison of competitive profiles across the Spanish football leagues

Published Online: 29 Dec 2017
Page range: 207 - 220

Abstract

Abstract

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 < .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 < .05) in the set of categories analysed.

Keywords

  • Spanish football
  • competitive profiles
  • professional leagues
  • amateurs
  • youth and women
5 Articles
Open Access

Automated Feedback Selection for Robot-Assisted Training

Published Online: 29 Dec 2017
Page range: 149 - 174

Abstract

Abstract

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.

Keywords

  • virtual trainer
  • rowing simulator
  • motor learning
  • linear mixed models
  • statistical learning
Open Access

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

Published Online: 29 Dec 2017
Page range: 175 - 184

Abstract

Abstract

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 x,y 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 (r = 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.

Keywords

  • motion analysis
  • football
  • match analysis
  • performance analysis
Open Access

A Logistic Regression/Markov Chain Model for American College Football

Published Online: 29 Dec 2017
Page range: 185 - 196

Abstract

Abstract

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.

Keywords

  • logistic regresion
  • Markov chain
  • American college football
  • common game
  • margin of victory
Open Access

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

Published Online: 29 Dec 2017
Page range: 197 - 206

Abstract

Abstract

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, F(2.74, 65.86)=0.65, p=.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), t(24)=3.03, p=.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.

Keywords

  • exergames
  • energy expenditure
  • HR
Open Access

A comparison of competitive profiles across the Spanish football leagues

Published Online: 29 Dec 2017
Page range: 207 - 220

Abstract

Abstract

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 < .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 < .05) in the set of categories analysed.

Keywords

  • Spanish football
  • competitive profiles
  • professional leagues
  • amateurs
  • youth and women

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