Using deep neural networks to detect non-analytically defined expert event labels in canoe sprint force sensor signals
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910890671996928 |
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| author | Rockstroh, Sarah Frenzel, Patrick Matthes, Daniel Schubert, Kay Wollburg, David Fuchs, Mirco |
| author_facet | Rockstroh, Sarah Frenzel, Patrick Matthes, Daniel Schubert, Kay Wollburg, David Fuchs, Mirco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using deep neural networks to detect non-analytically defined expert event labels in canoe sprint force sensor signals Rockstroh, Sarah Frenzel, Patrick Matthes, Daniel Schubert, Kay Wollburg, David Fuchs, Mirco Computer Vision and Pattern Recognition 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. |
| title | Using deep neural networks to detect non-analytically defined expert event labels in canoe sprint force sensor signals |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.08395 |