Development and evaluation of a self-training system for tennis shots with motion feature assessment and visualization
| Autor: | Masaki Oshita, Shigeru Kuriyama, Shunsuke Ineno, Takumi Inao, Tomohiko Mukai |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) 020207 software engineering Statistical model 02 engineering and technology Computer Graphics and Computer-Aided Design Motion capture Motion (physics) Visualization Computer graphics Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Self training Software |
| Zdroj: | The Visual Computer. 35:1517-1529 |
| ISSN: | 1432-2315 0178-2789 |
| DOI: | 10.1007/s00371-019-01662-1 |
| Popis: | In this paper, we propose a prototype of a self-training system for tennis forehand shots that allows trainees to practice their motion forms by themselves. Our system includes a motion capture device to record the trainee’s motion, and the system visualizes the differences between the features of the trainee’s motion and the correct motion performed by an expert. The system enables trainees to understand the errors in their motion and how to reduce or eliminate them. In this study, we classify the motion features and corresponding visualization methods based on the one-dimensional spatial, rotational, and temporal features of key poses. We also develop a statistical model for the motion features so that the system can assess and prioritize all features of a trainee’s motion. Related features are simultaneously visualized by analyzing their correlations. We describe the process of defining the motion features for the tennis forehand shot of an expert. We evaluated our prototype through several user experiments and demonstrated its feasibility as a self-training system. |
| Databáze: | OpenAIRE |
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