Abstract

In this study, inertial measurement units (IMUs) were used to train a random forest classifier to correctly classify different jump types in volleyball. Athlete motion data were collected in a controlled setting using three IMUs, one on the waist and one on each ankle. There were 11 participants who at the time played volleyball at the collegiate level in the United States, seven male and four female. Each performed the same number of jumps across the eight jump types--five BASIC jumps and three each of the other seven--resulting in 26 jumps per subject for a total of 286. The data were processed using a max-bin method and trained using a leave-one-out cross-validation method to produce a classifier that can determine jump type with an accuracy of 0.967, as measured by an F1-score.

Degree

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

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