Predicting Cross-Country Skiing Techniques Using Machine Learning
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Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
SAVYA SACHI, GUPTA
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Power meters are vital for a cyclist during training and competitions as a tool for
evaluation and improvement. Expanding their application to other sports would
help athletes train better during workouts and maximize performance. In crosscountry
skiing, power comes from the poles and highly depends on skiing technique
used. Thus, in order to predict power, it is important to predict which technique
is being used. This thesis focuses on evaluating two machine learning methods to
predict techniques in cross-country skiing. Data is collected in collaboration with
Skisens AB who provided the sensors and Ulricehamn’s skidgymnasium who helped
in collecting data and experimental planning. Data was collected on a treadmill and
on roller skis in an outdoor setting to get a balanced set of data and evaluations.
Random forests and LSTM networks were selected as the two methods for evaluation.
10 fold cross validation was performed on each model after hyperparameter tuning
and the overall accuracy, balanced accuracy and MCC score were recorded. Random
forests with a reduced feature set achieved an overall accuracy of 74.4%, while the
accuracy of treadmill data and outdoor data was 89.8% and 85.8% respectively. The
LSTM model achieved an overall accuracy of 86.2%, while the accuracy of treadmill
data and outdoor data was 86.5% and 84.1% respectively.
Beskrivning
Ämne/nyckelord
Cross country skiing , machine learning , random forest , neural network , force , analytics , data science , sports , gait analysis , skiing techniques , science , engineering.