  Assessing an athlete's performance in canoe sprint is often established by
measuring a variety of kinematic parameters during training sessions. Many of
these parameters are related to single or multiple paddle stroke cycles.
Determining on- and offset of these cycles in force sensor signals is usually
not straightforward and requires human interaction. This paper explores
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in
terms of their ability to automatically predict these events. In addition, our
work proposes an extension to the recently published SoftED metric for event
detection in order to properly assess the model performance on time windows. In
our results, an RNN based on bidirectional gated recurrent units (BGRUs) turned
out to be the most suitable model for paddle stroke detection.
