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Chapter

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Title

Movement Pattern Recognition in Boxing Using Raw Inertial Measurements

Authors

[ 1 ] Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication

2024

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • activity recognition
  • artificial intelligence
  • artificial neural networks
  • edge computing
  • microcontrollers
Abstract

EN In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller to classify boxing punches. Artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer’s wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.

Date of online publication

03.02.2024

Pages (from - to)

19 - 34

URL

https://link.springer.com/chapter/10.1007/978-3-031-53036-4_2

Book

Optimization, Learning Algorithms and Applications : Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27-29, 2023. Revised selected papers, part II

Presented on

Third International Conference OL2A 2023, 27-29.09.2023, Ponta Delgada/Azores, Portugal

Ministry points / chapter

20

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