Sport motion picture analysis as time series data

Maeda, Toshiyuki; Fujii, Masanori; Hayashi, Isao

doi:10.1109/amc.2014.6823337

Cited by 1 publication

(4 citation statements)

References 8 publications

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“…Research of strategies most used for the recognition and classification of human movement patterns. [23] Analysis of sports skills data with temporal series image data retrieved from films focused on table tennis.…”

Section: Rq7: Appliedmentioning

confidence: 99%

“…The ACO uses a similar method to solve optimization problems [64,65]. - [23] Moving images of 15 male college tennis players. [33] Data of Laguna Poreč half-marathon (2017) and Ormož half-marathon (2017).…”

Section: Rq7: Appliedmentioning

confidence: 99%

“…[50] - Paper Algorithm [27] Artificial Neural Networks, Statistical Classifiers and Hidden Markov Models. [23] C4.5, Random Forest and Native Bayes Tree. [33] Differential Evolution.…”

Section: Rq7: Appliedmentioning

confidence: 99%

“…Advances in Human-Computer Interaction Paper Proposal/Result [27] Research of strategies most used for the recognition and classification of human movement patterns. [23] Analysis of sports skills data with temporal series image data retrieved from films focused on table tennis. [33] Guide the athletes on how to improve their performance and how to eliminate errors related to the selection of the proper running strategy through the differential evolution algorithm.…”

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confidence: 99%

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Computational Intelligence in Sports: A Systematic Literature Review

Bonidia

Rodrigues

Avila-Santos

et al. 2018

Advances in Human-Computer Interaction

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Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

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Section: Rq7: Appliedmentioning

confidence: 99%

Section: Rq7: Appliedmentioning

confidence: 99%

“…[50] - Paper Algorithm [27] Artificial Neural Networks, Statistical Classifiers and Hidden Markov Models. [23] C4.5, Random Forest and Native Bayes Tree. [33] Differential Evolution.…”

Section: Rq7: Appliedmentioning

confidence: 99%

mentioning

confidence: 99%

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