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Can machine learning distinguish between elite and non-elite rowers?

Kristine Fjellkårstad Orten

Kristine Fjellkårstad Orten

Department of Informatics, University of OsloOslo, Norway
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Orten, Kristine Fjellkårstad
, 
Sander Elias Magnussen Helgesen

Sander Elias Magnussen Helgesen

Department of Informatics, University of OsloOslo, Norway
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Helgesen, Sander Elias Magnussen
, 
Bihui Chen

Bihui Chen

Department of Informatics, University of OsloOslo, Norway
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Chen, Bihui
, 
Adel Baselizadeh

Adel Baselizadeh

  • Department of Informatics, University of OsloOslo, Norway
  • RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of OsloOslo, Norway
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Baselizadeh, Adel
, 
Jim Torresen

Jim Torresen

  • Department of Informatics, University of OsloOslo, Norway
  • RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of OsloOslo, Norway
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Torresen, Jim
 oraz 
Henrik Herrebrøden

Henrik Herrebrøden

Department of Psychology, Pedagogy and Law, School of Health Sciences, Kristiania University CollegeOslo, Norway
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Herrebrøden, Henrik
  
01 maj 2025
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Data publikacji: 01 maj 2025
Zakres stron: 118 - 132
DOI: https://doi.org/10.2478/ijcss-2025-0007
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© 2025 Kristine Fjellkårstad Orten et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

A rower using the rowing ergometer with reflective markers (marked with red).
A rower using the rowing ergometer with reflective markers (marked with red).

Figure 2:

Sequence splitting. Example visualization of rowing sequence input data. The sequence of rowing strokes was divided into multiple parts of single strokes (dark red box) with a 0.25-sec error margin (light red) to ensure a complete stroke was included. Each new sequence had a 1-second overlap with the previous sequence.
Sequence splitting. Example visualization of rowing sequence input data. The sequence of rowing strokes was divided into multiple parts of single strokes (dark red box) with a 0.25-sec error margin (light red) to ensure a complete stroke was included. Each new sequence had a 1-second overlap with the previous sequence.

Figure 3:

The GRU-CNN model extended the initial GRU model with a CNN and more layers. The input (1) was sent through a convolutional layer (2) with 128 filters, a kernel size of 3 and ReLU activation, then a max pooling layer (3) with pool size 2, then two GRU layers (4, 5) with the same parameters as the original GRU model. At last, the data is sent through a dense layer (6) with 128 units and ReLU activation, then a dropout layer (7) with a dropout rate of 0.3 and then a final one-unit dense layer (8) with sigmoid activation.
The GRU-CNN model extended the initial GRU model with a CNN and more layers. The input (1) was sent through a convolutional layer (2) with 128 filters, a kernel size of 3 and ReLU activation, then a max pooling layer (3) with pool size 2, then two GRU layers (4, 5) with the same parameters as the original GRU model. At last, the data is sent through a dense layer (6) with 128 units and ReLU activation, then a dropout layer (7) with a dropout rate of 0.3 and then a final one-unit dense layer (8) with sigmoid activation.

Figure 4:

Rowing angles. Example visualization of the second type of input features: Angles between reflective markers. From the left: α, β and γ.
Rowing angles. Example visualization of the second type of input features: Angles between reflective markers. From the left: α, β and γ.

Figure 5:

Violin plot showing validation and test accuracies for different feature combinations in the GRU model. The mean value and range are marked in the plot. The mean was computed across 10 instances of training the model. The left-most plot shows the accuracies achieved for different input features when using the validation data set, while the right-most plot shows the accuracies for the test data set.
Violin plot showing validation and test accuracies for different feature combinations in the GRU model. The mean value and range are marked in the plot. The mean was computed across 10 instances of training the model. The left-most plot shows the accuracies achieved for different input features when using the validation data set, while the right-most plot shows the accuracies for the test data set.

Figure 6:

10-fold cross-validation results for the GRU-CNN model. The mean value and range are marked in the plot.
10-fold cross-validation results for the GRU-CNN model. The mean value and range are marked in the plot.

Figure 7:

Training and validation accuracies for the MLP model for coordinate input feature combinations.
Training and validation accuracies for the MLP model for coordinate input feature combinations.

Performance metrics for the best-trained GRU-CNN model when evaluated on the test dataset_

Input features F1 score AUC Accuracy
Coords Shoulders and hips 0.4226 0.4026 0.5181
Shoulders and seat 0.4150 0.4460 0.5181
Shoulders and ergometer front 0.6989 0.5376 0.7744
Ergometer handle and front 0.6792 0.5368 0.7632
Hips and ergometer front 0.5222 0.5117 0.5209

Performance metrics for the best-trained MLP model when evaluated on the test dataset_

Input features F1 score AUC Accuracy
Coords Shoulders and hips 0.2836 0.9962 0.6178
Shoulders and seat 0.3557 0.9949 0.6591
Shoulders and ergometer front 0.9328 0.9996 0.9482
Ergometer handle and front 0.9190 0.9999 0.9387
Hips and ergometer front 0.6812 0.9948 0.8365
All joints 1 1 1
Język:
Angielski
Częstotliwość wydawania:
2 razy w roku
Dziedziny czasopisma:
Informatyka, Bazy danych i eksploracja danych, Informatyka, inne, Sport i rekreacja, Wychowanie fizyczne, Sport i rekreacja, inne
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