Visualization of table tennis skill by neural networks and fuzzy inference - Hayashi, I.; Irie, H.; Maeda, T.; Fujii, M.; Tasaka, T. (Proceedings Book of The 15th ITTF Sports Science Congress Düsseldorf, 27th - 28th May 2017, 2017)
The Topographic Attentive Mapping (TAM) network is a biologically-inspired classifier that bears similarities to the human visual system. When used in a TAM network, the proposed pruning algorithm improves classification accuracy and allows extracting knowledge as represented by the network structure. Fuzzy Inference is a representation method of mapping inputs to outputs using fuzzy set theory. Fuzzy inference has been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. In this paper, sport technique evaluation of motion analysis modelled by TAM network and Fuzzy Inference is discussed. The trajectory pattern of forehand strokes of table tennis players is analyzed with nine sensor markers attached to the right upper arm of players. With the TAM network and Fuzzy Inference, technique rules are extracted by learning algorithm in order to classify the skill level of players of table tennis from the sensor data. In addition, the difference between the elite player, middle level player and beginner is visualized, and how to improve skills specific to table tennis from the view of data analysis is discussed.