The Classification of Badminton Strokes: A Feature Importance Investigation

Qiyang Li; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa; Muhammad Amirul Abdullah; Sze Hong Teh; Chenguang Liu; Eng Hwa Yap

doi:10.1007/978-981-99-8498-5_35

The Classification of Badminton Strokes: A Feature Importance Investigation

Qiyang Li, Anwar P. P. Abdul Majeed^*, Rabiu Muazu Musa, Muhammad Amirul Abdullah, Sze Hong Teh, Chenguang Liu, Eng Hwa Yap

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This work employed the Mean Decrease Impurity (MDI) feature selection technique in classifying different badminton strokes. An online repository that consists of data acquired from an Inertial Measurement Unit of players executing five distinct strokes were used in the study. A total of 104 statistical features were extracted from the data. A vanilla Random Forest model was used to classify the strokes based on all the features extracted as well as features identified via the MDI technique. The dataset was split into an 80:20 ratio for training and testing. It was demonstrated from the study that a total of 59 features were identified to be significant that could yield a comparable testing accuracy. The findings suggest that MDI streamlined the important features whilst discarding redundant and less informative features. This allow for a more computationally efficient model to be developed and practically deployed without sacrificing its predictive power.

Original language	English
Title of host publication	Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
Editors	Andrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	439-442
Number of pages	4
ISBN (Print)	9789819984978
DOIs	https://doi.org/10.1007/978-981-99-8498-5_35
Publication status	Published - 2024
Event	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China Duration: 22 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	845
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023
Country/Territory	China
City	Suzhou
Period	22/08/23 → 23/08/23

Keywords

Badminton
Feature importance
Machine learning
Performance evaluation
Sports
Wearables

Access to Document

10.1007/978-981-99-8498-5_35

Cite this

Li, Q., P. P. Abdul Majeed, A., Musa, R. M., Abdullah, M. A., Teh, S. H., Liu, C., & Yap, E. H. (2024). The Classification of Badminton Strokes: A Feature Importance Investigation. In A. Tan, F. Zhu, H. Jiang, K. Mostafa, E. H. Yap, L. Chen, L. J. A. Olule, & H. Myung (Eds.), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023 (pp. 439-442). (Lecture Notes in Networks and Systems; Vol. 845). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8498-5_35

Li, Qiyang ; P. P. Abdul Majeed, Anwar ; Musa, Rabiu Muazu et al. / The Classification of Badminton Strokes : A Feature Importance Investigation. Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. editor / Andrew Tan ; Fan Zhu ; Haochuan Jiang ; Kazi Mostafa ; Eng Hwa Yap ; Leo Chen ; Lillian J. A. Olule ; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. pp. 439-442 (Lecture Notes in Networks and Systems).

@inproceedings{40373981d61d484da25f178d53a677b9,

title = "The Classification of Badminton Strokes: A Feature Importance Investigation",

abstract = "This work employed the Mean Decrease Impurity (MDI) feature selection technique in classifying different badminton strokes. An online repository that consists of data acquired from an Inertial Measurement Unit of players executing five distinct strokes were used in the study. A total of 104 statistical features were extracted from the data. A vanilla Random Forest model was used to classify the strokes based on all the features extracted as well as features identified via the MDI technique. The dataset was split into an 80:20 ratio for training and testing. It was demonstrated from the study that a total of 59 features were identified to be significant that could yield a comparable testing accuracy. The findings suggest that MDI streamlined the important features whilst discarding redundant and less informative features. This allow for a more computationally efficient model to be developed and practically deployed without sacrificing its predictive power.",

keywords = "Badminton, Feature importance, Machine learning, Performance evaluation, Sports, Wearables",

author = "Qiyang Li and {P. P. Abdul Majeed}, Anwar and Musa, {Rabiu Muazu} and Abdullah, {Muhammad Amirul} and Teh, {Sze Hong} and Chenguang Liu and Yap, {Eng Hwa}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 ; Conference date: 22-08-2023 Through 23-08-2023",

year = "2024",

doi = "10.1007/978-981-99-8498-5_35",

language = "English",

isbn = "9789819984978",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "439--442",

editor = "Andrew Tan and Fan Zhu and Haochuan Jiang and Kazi Mostafa and Yap, {Eng Hwa} and Leo Chen and Olule, {Lillian J. A.} and Hyun Myung",

booktitle = "Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023",

}

Li, Q, P. P. Abdul Majeed, A, Musa, RM, Abdullah, MA, Teh, SH , Liu, C & Yap, EH 2024, The Classification of Badminton Strokes: A Feature Importance Investigation. in A Tan, F Zhu, H Jiang, K Mostafa, EH Yap, L Chen, LJA Olule & H Myung (eds), Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Lecture Notes in Networks and Systems, vol. 845, Springer Science and Business Media Deutschland GmbH, pp. 439-442, International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023, Suzhou, China, 22/08/23. https://doi.org/10.1007/978-981-99-8498-5_35

The Classification of Badminton Strokes: A Feature Importance Investigation. / Li, Qiyang; P. P. Abdul Majeed, Anwar; Musa, Rabiu Muazu et al.
Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. ed. / Andrew Tan; Fan Zhu; Haochuan Jiang; Kazi Mostafa; Eng Hwa Yap; Leo Chen; Lillian J. A. Olule; Hyun Myung. Springer Science and Business Media Deutschland GmbH, 2024. p. 439-442 (Lecture Notes in Networks and Systems; Vol. 845).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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T1 - The Classification of Badminton Strokes

T2 - International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

AU - Li, Qiyang

AU - P. P. Abdul Majeed, Anwar

AU - Musa, Rabiu Muazu

AU - Abdullah, Muhammad Amirul

AU - Teh, Sze Hong

AU - Liu, Chenguang

AU - Yap, Eng Hwa

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - This work employed the Mean Decrease Impurity (MDI) feature selection technique in classifying different badminton strokes. An online repository that consists of data acquired from an Inertial Measurement Unit of players executing five distinct strokes were used in the study. A total of 104 statistical features were extracted from the data. A vanilla Random Forest model was used to classify the strokes based on all the features extracted as well as features identified via the MDI technique. The dataset was split into an 80:20 ratio for training and testing. It was demonstrated from the study that a total of 59 features were identified to be significant that could yield a comparable testing accuracy. The findings suggest that MDI streamlined the important features whilst discarding redundant and less informative features. This allow for a more computationally efficient model to be developed and practically deployed without sacrificing its predictive power.

AB - This work employed the Mean Decrease Impurity (MDI) feature selection technique in classifying different badminton strokes. An online repository that consists of data acquired from an Inertial Measurement Unit of players executing five distinct strokes were used in the study. A total of 104 statistical features were extracted from the data. A vanilla Random Forest model was used to classify the strokes based on all the features extracted as well as features identified via the MDI technique. The dataset was split into an 80:20 ratio for training and testing. It was demonstrated from the study that a total of 59 features were identified to be significant that could yield a comparable testing accuracy. The findings suggest that MDI streamlined the important features whilst discarding redundant and less informative features. This allow for a more computationally efficient model to be developed and practically deployed without sacrificing its predictive power.

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KW - Performance evaluation

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BT - Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023

A2 - Tan, Andrew

A2 - Zhu, Fan

A2 - Jiang, Haochuan

A2 - Mostafa, Kazi

A2 - Yap, Eng Hwa

A2 - Chen, Leo

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A2 - Myung, Hyun

PB - Springer Science and Business Media Deutschland GmbH

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Li Q, P. P. Abdul Majeed A, Musa RM, Abdullah MA, Teh SH , Liu C et al. The Classification of Badminton Strokes: A Feature Importance Investigation. In Tan A, Zhu F, Jiang H, Mostafa K, Yap EH, Chen L, Olule LJA, Myung H, editors, Advances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 439-442. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-8498-5_35

The Classification of Badminton Strokes: A Feature Importance Investigation

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