The purpose of the study was to assign rowers to different rowing events based on their demographics and rowing kinematics using machine learning models. A total of 55 elite athletes from the Chinese National Rowing Team participated, each instructed to row on a rowing ergometer for one minute at three stroke rates: 18, 26, and 32 strokes/min. Trunk and upper arm 3D kinematics were collected using an inertia measurement unit 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 the collected demographics and kinematic data to classify rowers` groups (i.e. coxed eight and single/pair event group). The machine learning models successfully classified rowers` groups, with the top-performing models (decision tree, extreme gradient boosting, and random forest) achieving high classification performance (accurate rate = 0.89-0.93). The rowing event assignment automated by machine learning may help coaches make more informed and objective decisions. By minimising subjective biases, this approach enhances the accuracy and fairness of athlete selection processes, thereby potentially optimising team composition and performance outcomes.
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