An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Rafael Cuperman*, Kaspar M.B. Jansen*, Michal G. Ciszewski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
64 Downloads (Pure)

Abstract

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks.

Original languageEnglish
Article number1347
Pages (from-to)1-27
Number of pages27
JournalSensors
Volume22
Issue number4
DOIs
Publication statusPublished - 2022

Keywords

  • Artificial neural networks
  • Convolutional neural networks
  • Deep learning
  • Football
  • HAR
  • Human activity recognition
  • IMU
  • LSTM
  • Machine learning
  • Soccer

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