Predicting Countermovement Jump Heights by Time Domain, Frequency Domain, and Machine Learning Algorithms

Zhou, Zhaoxian; Shakya, Sarbagya Ratna; Sha, Zhanxin

doi:10.1109/iscid.2017.162

Cited by 2 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even if the different data were created using different sport branches, sexes, and physical properties, similar results were obtained via linear regression with the same exercise protocol. The R 2 score of the CMJF was 0.70 in this study, and 0.689 was achieved by Zhou et al [25]. However, the RBFNN achieved an R 2 score of 0.96 in the same experiment.…”

Section: B Determination Of Factorssupporting

confidence: 54%

“…The mean absolute error was used as an evaluation criterion, and it was concluded that the ANN was superior for minimizing error. The most similar study to the present study, which used countermovement jump experiments, was performed by Zhou et al [25] to predict the heights of countermovement jumps with free hands in football players. The comparison was performed among linear regression, decision tree and random forest models.…”

Section: B Determination Of Factorsmentioning

confidence: 97%

“…Zhou et al [25] conducted research to predict countermovement jump heights by using machine learning models. Decision tree, random forest, and linear regression models were implemented to train selected features of athletes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Oytun¹,

Tınazcı²,

Şekeroğlu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players' performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players' athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radialbasis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with R 2 scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies. INDEX TERMS Artificial intelligence, athletic performance, machine learning models, radial-basis function neural network.

show abstract

Section: B Determination Of Factorssupporting

confidence: 54%

Section: B Determination Of Factorsmentioning

confidence: 97%

See 1 more Smart Citation

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Oytun¹,

Tınazcı²,

Şekeroğlu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

Rajšp

Fister

2020

Applied Sciences

View full text Add to dashboard Cite

The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

show abstract

Predicting Countermovement Jump Heights by Time Domain, Frequency Domain, and Machine Learning Algorithms

Cited by 2 publications

References 11 publications

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

Contact Info

Product

Resources

About