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%).<br>
<br><pre>@inproceedings{Ebner_19_TennisStrokeClassification,
  author = {Ebner, Christopher J. and Findling, Rainhard Dieter},
  booktitle = {Proc. {MoMM} 2019: 17th International Conference on
  		  Advances in Mobile Computing and Multimedia},
  title = {Tennis Stroke Classification: Comparing Wrist and Racket
  		  as IMU Sensor Position},
  year = {2019},
  month = dec,
  number = { {In print}},
  publisher = {ACM}
}
</pre>