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

Abstract

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.

Can Slow-Motion Footage of Forehand Strokes Be Used to Immediately Improve Anticipatory Judgments in Tennis?

“…These results indicate that skilled players can pick up key kinematic cues from the CG avatar for effective anticipation when compared with their novice counterparts. These results are comparable with those of previous studies that used videos ( Williams et al, 2002 ; Jackson and Mogan, 2007 ), point-light or stick figure displays ( Ward et al, 2002 ; Huys et al, 2009 ; Williams et al, 2009 ), and CG animations ( Fukuhara et al, 2009 ; Ida et al, 2012 ; Fukuhara et al, 2017 ). From these findings, we can safely say that our CG animations have sufficient quality for evaluating anticipatory skills in tennis.…”

“…Second, a CG tennis avatar (e.g., Ida et al, 2012 ; Fukuhara et al, 2017 ) was constructed from the motion capture data using character animation software (MotionBuilder 2013, Autodesk Inc.). The character modeling and AVI exporting were conducted with 3DCG software (Maya 2013, Autodesk Inc.).…”

The role of proximal body information on anticipatory judgment in tennis using graphical information richness

“…To rule out this possibility, we developed a novel methodological technique in which computer graphics (CG) stimuli of the whole body, the racket, and the ball were displayed. The whole body was presented using a combination of point-light display (PLD) (i.e., presenting minimal kinematic information of body landmark points) (e.g., [ 12 , 13 ]) and polygon display (i.e., presenting human-like body, such as clothing, skin and limb configuration) (e.g., [ 14 – 17 ]). PLD has sufficient visual information to allow the observer to recognize intentions underlying other people’s actions and such visual perception is widely known as biological motion perception [ 12 , 13 ].…”

Virtual Reality Technologies in Telecommunication Services

“…On the other hand, interesting findings have been obtained with other systems. In observing and analyzing motions, observers felt the use of 3D CG-based representation speeded up the opponent's motions relative to what they would have been with simplified stick figure models [79] (Fig. 2).…”