Recognition of Tennis Serve Performed by a Digital Player: Comparison among Polygon, Shadow, and Stick-Figure Models

The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.