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 proceedingConference Proceedingpeer-review


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 languageEnglish
Title of host publicationAdvances in Intelligent Manufacturing and Robotics - Selected Articles from ICIMR 2023
EditorsAndrew Tan, Fan Zhu, Haochuan Jiang, Kazi Mostafa, Eng Hwa Yap, Leo Chen, Lillian J. A. Olule, Hyun Myung
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages4
ISBN (Print)9789819984978
Publication statusPublished - 2024
EventInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023 - Suzhou, China
Duration: 22 Aug 202323 Aug 2023

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent Manufacturing and Robotics, ICIMR 2023


  • Badminton
  • Feature importance
  • Machine learning
  • Performance evaluation
  • Sports
  • Wearables


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