Abstract
Four expert rowers’ gestures were gathered on the SPRINT rowing platform with the aid of an optic motion tracking system. Data were analyzed in order to get a digital representation of the features involved in rowing. Moreover, these data provide a dataset for developing digital models for rowing motion synthesis. Rowers were modeled as kinematic chains, data were processed in order to get position and orientation of upper body limbs. This representation was combined with SPRINT data in order to evaluate features found in the literature, to find new ones and to build models for the generation of rowing motion. The analysis shows the effectiveness of the motion reconstruction and two examples of technique features: stroke timing and upper limbs orientation during the finish phase.
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