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

Rainhard Findling, Christopher J. Ebner

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

8 Citations (Scopus)
190 Downloads (Pure)


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 & Multimedia (MoMM2019)
Number of pages10
ISBN (Electronic)978-1-4503-7178-0
Publication statusPublished - Dec 2019
MoE publication typeA4 Conference publication
EventInternational Conference on Advances in Mobile Computing and Multimedia - Munich, Germany
Duration: 2 Dec 20194 Dec 2019
Conference number: 17


ConferenceInternational Conference on Advances in Mobile Computing and Multimedia
Abbreviated titleMoMM


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


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