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

Rainhard Findling, Christopher J. Ebner

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

Abstract

Automatic tennis stroke recognition can help tennis players im-
prove their training experience. Previous work has used sensors
positions on both wrist and tennis racket, of which different physi-
ological 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 posi-
tions 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)
PublisherACM
Number of pages10
ISBN (Electronic)978-1-4503-7178-0
DOIs
Publication statusAccepted/In press - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advances in Mobile Computing and Multimedia - Munich, Germany
Duration: 2 Dec 20194 Dec 2019
Conference number: 17

Conference

ConferenceInternational Conference on Advances in Mobile Computing and Multimedia
Abbreviated titleMoMM
CountryGermany
CityMunich
Period02/12/201904/12/2019

Keywords

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

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  • Cite this

    Findling, R., & Ebner, C. J. (Accepted/In press). Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position. In 17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019) ACM. https://doi.org/10.1145/3365921.3365929