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%).
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 language | English |
|---|---|
| Title of host publication | 17th International Conference on Advances in Mobile Computing & Multimedia (MoMM2019) |
| Publisher | ACM |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-4503-7178-0 |
| DOIs | |
| Publication status | Accepted/In press - 2019 |
| MoE publication type | A4 Article in a conference publication |
| Event | International Conference on Advances in Mobile Computing and Multimedia - Munich, Germany Duration: 2 Dec 2019 → 4 Dec 2019 Conference number: 17 |
Conference
| Conference | International Conference on Advances in Mobile Computing and Multimedia |
|---|---|
| Abbreviated title | MoMM |
| Country | Germany |
| City | Munich |
| Period | 02/12/2019 → 04/12/2019 |
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