Extraction of Knowledge from the Topographic Attentive Mapping Network and its Application in Skill Analysis of Table Tennis

The Topographic Attentive Mapping (TAM) network is a biologically-inspired classifier that bears similarities to the human visual system. In case of wrong classification during training, an attentional top-down signal modulates synaptic weights in intermediate layers to reduce the difference between the desired output and the classifier’s output. When used in a TAM network, the proposed pruning algorithm improves classification accuracy and allows extracting knowledge as represented by the network structure. In this paper, sport technique evaluation of motion analysis modelled by the TAM network was discussed. The trajectory pattern of forehand strokes of table tennis players was analyzed with nine sensor markers attached to the right upper arm of players. With the TAM network, input attributes and technique rules were extracted in order to classify the skill level of players of table tennis from the sensor data. In addition, differences between the elite player, middle level player and beginner were clarified; furthermore, we discussed how to improve skills specific to table tennis from the view of data analysis.