TY - GEN
T1 - Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position
AU - Ebner, Christopher
AU - Findling, Rainhard Dieter
N1 - Winner of the MoMM 2019 best paper award
Publisher Copyright:
© 2019 ACM.
PY - 2019/12/2
Y1 - 2019/12/2
N2 - 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%).
AB - 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%).
KW - machine learning
KW - tennis stroke detection
KW - tennis stroke recognition
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85098274984&partnerID=8YFLogxK
U2 - 10.1145/3365921.3365929
DO - 10.1145/3365921.3365929
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 74
EP - 83
BT - 17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019 - Proceedings
A2 - Haghighi, Pari Delir
A2 - Salvadori, Ivan Luiz
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Anderst-Kotsis, Gabriele
ER -