loso
Here are 4 public repositories matching this topic...
Three-class football player fatigue prediction from PAMAP2 wearable IoT data. Karvonen heart rate labeling, SMOTE balancing, LOSO cross-validation, personalized Random Forest. 97.96% LOSO accuracy + coach substitution-alert dashboard.
-
Updated
Apr 21, 2026 - Jupyter Notebook
Classifies football players into Attacker, Midfielder, Defender roles from PAMAP2 IoT wearable data. LSTM, BiLSTM, and TCN-Transformer architectures. 99.24% accuracy, LOSO 98.89%±0.42%. SHAP sensor attribution per role.
-
Updated
Apr 21, 2026 - Jupyter Notebook
Improve this page
Add a description, image, and links to the loso topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the loso topic, visit your repo's landing page and select "manage topics."