The purpose was to assign rowers to different rowing events based on their demographics and rowing kinematics using machine learning models. 55 elite athletes were instructed to row on a rowing ergometer for one minute at three stroke rates. Trunk, pelvis, and shoulder 3D kinematics were collected using an IMU system at a sampling rate of 100 Hz. Trunk and upper arm segmental and joint range of motion were generated. Trunk segments and upper arm motion coordination were analysed using the vector coding method. Six supervised machine learning models were trained using demographic and kinematic features to classify rowers` groups. The machine learning models successfully classified rowers` groups (accuracy up to 0.94). The rowing event assignment automated by machine learning may help coaches make more informed and objective decisions.
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