Analysis of the Smash and Dropshot Movement Patterns in Badminton Sports using Pearson Correlation

Kurnia, Rahmadi; Pasmai, Abdian; Elfitri, Ikhwana

doi:10.1145/3397391.3397434

Cited by 2 publications

(19 citation statements)

References 3 publications

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“…The 15 articles included eight journal articles [2][3][4][5][6]25,33,36 and seven conference papers. 12,27,34,35,[38][39][40] Five articles focused on tennis (forehand stroke, 35,36 serve 2,33 and both the forehand stroke and serve 34 ), five articles on table tennis (forehand stroke, 3,6,25 backhand block 38 and block 27 ), four articles on badminton (smash, 39,40 backhand long serve 4 and smash and drop shot 12 ) and one article on squash (forehand drive, backhand drive, volley and drop shot 5 ). Between 1 4,33 and 20 38 players participated in each study, with seven studies focusing on adult players (mean age of over 18 years), 2,3,6,25,36,38,40 1 study focusing on junior players (mean age below 18 years) 5 while the age of the players was not specified in the remaining studies.…”

Section: Resultsmentioning

confidence: 99%

“…For the player level, experienced players (level described as being able to accurately perform the different stroke techniques) were used in nine studies, [2][3][4][5][6]25,36,39 amateur level players (level described as being able to not accurately perform the different stroke techniques) were not used in any studies, while five studies included both levels of players. 12,27,34,35,38,40 One study did not specify the player level. 33 Warm-up procedures were performed in seven studies 2,3,5,25,27,36,40 and the total number of strokes hit by each player during the testing conditions for the study varied between 1 4,34 and 150.…”

Section: Resultsmentioning

confidence: 99%

“…6,40 Commercial devices were used in eight studies 2,3,5,6,25,34,38,40 and custom-built devices were used in seven studies. 4,12,27,33,35,36,39 In the studies using Table 1. An example of a full electronic key word search term strings for one database.…”

Section: Resultsmentioning

confidence: 99%

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Application of wearable technologies for player motion analysis in racket sports: A systematic review

Rigozzi

Vio

Poronnik

2022

International Journal of Sports Science & Coaching

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The use of wearable devices for player motion analysis is becoming a popular method to measure kinematic parameters associated with player techniques in racket sports. This systematic review focused on identifying the current applications of wearable technology for player motion analysis in racket sports (tennis, table tennis, badminton and squash) through two research questions: (1) What are the existing applications of wearable technology for player motion analysis in racket sports? (2) What data analysis methods are used to quantify and evaluate player motion? A comprehensive search of MEDLINE, EMBASE, SPORTDiscus, Scopus, Web of Science, and IEEE databases was undertaken following PRISMA reporting guidelines. Included studies must have only used external wearable technology mounted to either the player or the racket for the potential application of player motion analysis in a racket sport, tested the wearable technology under normal playing conditions and not only focused on detecting/classifying the player's stroke or activity movement. Of the 6616 articles found, a total of 15 studies met the inclusion and exclusion criteria. Tennis and table tennis were the most popular sports researched and inertial measurement units and electromyography sensors were the most common types of sensors used. This review found that wearable devices were mainly used to analyze (1) movement similarities and differences of players at different playing levels, (2) variability in racket, upper limb and joint movement patterns and (3) movement differences associated with different ball spin levels. These technologies can create a new level of accessible personalized training in these sports.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Application of wearable technologies for player motion analysis in racket sports: A systematic review

Rigozzi

Vio

Poronnik

2022

International Journal of Sports Science & Coaching

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“…The first step towards data-set dimensionality in our case study was the determination of the correlation between all features. This procedure was achieved with the use of the Pearson’s correlation technique [ 29 ]. This technique is a powerful tool in finding redundancy within a data set.…”

Section: Proposed Approach Materials and Methodsmentioning

confidence: 99%

“…The 29 features were selected using redundancy analysis. The selection of those features was carried out by considering the interpretation of the correlation coefficient proposed by

[ 29 ]. The 29 features and their correlations are graphically represented with the heat map shown in Appendix A , and more concretely in Figure A1 .…”

Section: Proposed Approach Materials and Methodsmentioning

confidence: 99%

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Rubio‐Loyola

Paul-Fils

2022

Sensors

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Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.

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Analysis of the Smash and Dropshot Movement Patterns in Badminton Sports using Pearson Correlation

Cited by 2 publications

References 3 publications

Application of wearable technologies for player motion analysis in racket sports: A systematic review

Application of wearable technologies for player motion analysis in racket sports: A systematic review

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

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