Predicting Cross-Country Skiing Techniques Using Machine Learning

dc.contributor.authorSAVYA SACHI, GUPTA
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerJohansson, Moa
dc.contributor.supervisorKuylenstierna, Dan
dc.date.accessioned2021-09-20T07:38:44Z
dc.date.available2021-09-20T07:38:44Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractPower 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.sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304145
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectCross country skiingsv
dc.subjectmachine learningsv
dc.subjectrandom forestsv
dc.subjectneural networksv
dc.subjectforcesv
dc.subjectanalyticssv
dc.subjectdata sciencesv
dc.subjectsportssv
dc.subjectgait analysissv
dc.subjectskiing techniquessv
dc.subjectsciencesv
dc.subjectengineering.sv
dc.titlePredicting Cross-Country Skiing Techniques Using Machine Learningsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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