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

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Conference article in proceedings
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Date
2019-12
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Mcode
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Language
en
Pages
10
74–83
Series
17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019)
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-dependentand independent classification (89%-99%).
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Keywords
machine learning, tennis stroke detection, tennis stroke recognition, wearable sensors
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Citation
Findling , R & Ebner , C J 2019 , Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position . in 17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019) . ACM , pp. 74–83 , International Conference on Advances in Mobile Computing and Multimedia , Munich , Germany , 02/12/2019 . https://doi.org/10.1145/3365921.3365929