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Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors.

Alexander Hoelzemann1, Julia Lee Romero2, Marius Bock1

  • 1Ubiquitous Computing, University of Siegen, 57076 Siegen, Germany.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset for recognizing basketball activities using wrist sensors. It enables better sports analytics and training applications.

Keywords:
basketballdatasetwearable activity recognitionwrist-worn sensing

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Area of Science:

  • Sports Science
  • Human Activity Recognition
  • Wearable Sensor Technology

Background:

  • Wrist-worn inertial sensors are suitable for measuring basketball activities.
  • Accurate human activity recognition (HAR) in sports can enhance training and performance analysis.
  • Existing datasets may not capture the nuances of real-world basketball scenarios.

Purpose of the Study:

  • To present a novel benchmark dataset for evaluating HAR methods in basketball.
  • To facilitate the development of systems for game analysis, guided training, and personal activity tracking.
  • To capture inherent variances in basketball due to cultural differences and varying skill levels.

Main Methods:

  • Collected data from 24 players across two countries (USA and Germany) wearing wrist-worn inertial sensors.
  • Recorded data during both repetitive basketball training sessions and actual games.
  • Performed time-series analyses to characterize the dataset and evaluated baseline classification using deep learning architectures.

Main Results:

  • The dataset includes diverse basketball activities, training, drills, and games.
  • Variances in game rules, styles, and player skill levels are present.
  • A baseline study demonstrated the feasibility of using deep learning for activity classification.

Conclusions:

  • The presented dataset serves as a valuable resource for advancing HAR in basketball.
  • It supports the development of intelligent systems for sports performance enhancement.
  • Future research can leverage this dataset to explore more sophisticated activity recognition models.