Machine learning-enabled classification of climbers using small data

Athlete performance scoring within climbing presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill level is valuable as it can be used to mark training progress and help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. Solutions to the classification problem included light-weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Milburn, Nicholas

Contributors dc:contributor

• Liang, Yu
• Wu, Dalei; Hogg, Jennifer
• College of Engineering and Computer Science

• Data sets
• Machine learning
• Rock climbing

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/754

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1930