Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position

Christopher Ebner, Rainhard Dieter Findling

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

10 Citations (Scopus)

Abstract

Automatic tennis stroke recognition can help tennis players improve their training experience. Previous work has used sensors positions on both wrist and tennis racket, of which different physiological aspects bring different sensing capabilities. However, no comparison of the performance of both positions has been done yet. In this paper we comparatively assess wrist and racket sensor positions for tennis stroke detection and classification. We investigate detection and classification rates with 8 well-known stroke types and visualize their differences in 3D acceleration and angular velocity. Our stroke detection utilizes a peak detection with thresholding and windowing on the derivative of sensed acceleration, while for our stroke recognition we evaluate different feature sets and classification models. Despite the different physiological aspects of wrist and racket as sensor position, for a controlled environment results indicate similar performance in both stroke detection (98.5%-99.5%) and user-dependent and independent classification (89%-99%).
Original languageEnglish
Title of host publication17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019 - Proceedings
EditorsPari Delir Haghighi, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
Pages74-83
Number of pages10
ISBN (Electronic)9781450371780
DOIs
Publication statusPublished - 2 Dec 2019

Publication series

NameACM International Conference Proceeding Series

Keywords

  • machine learning
  • tennis stroke detection
  • tennis stroke recognition
  • wearable sensors

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