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

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Rules of Table Tennis Skill.
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j_hukin-2017-0005_fig_004: Rules of Table Tennis Skill.

Mentions: The set of linkages represented a fuzzy rule when we extracted linkages where wJi represented the maximum for each player. To express fuzzy rules for elite and beginner players, five categories with high pjk were extracted for each class. Since a category expresses a rule, these categories expressed the rules of the highest five pjk’s. In the first rule, wji of the marker on the racket in the horizontal direction was high, implying that this movement of the racket was important. The value of wji of each marker in the vertical direction was low, meaning that the swing movement was stable and without rough wavy movements. In both horizontal and vertical directions after the third rule, the value of wji was constant and the swing was stable. On the other hand, the vertical rise and fall motion was a salient feature of beginners’ motion patterns and was extracted as the first rule. The horizontal movement at the shoulder and the elbow was remarkable in the second rule, implying “a movement to delay the body”. Figure 4 shows the first rule of the elite player and beginner. In this Figure, the difference between the elite player and the beginner is shown very conspicuously, making it easy to formulate table tennis skills as a rule.


Extraction of Knowledge from the Topographic Attentive Mapping Network and its Application in Skill Analysis of Table Tennis
Rules of Table Tennis Skill.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC5304274&req=5

j_hukin-2017-0005_fig_004: Rules of Table Tennis Skill.
Mentions: The set of linkages represented a fuzzy rule when we extracted linkages where wJi represented the maximum for each player. To express fuzzy rules for elite and beginner players, five categories with high pjk were extracted for each class. Since a category expresses a rule, these categories expressed the rules of the highest five pjk’s. In the first rule, wji of the marker on the racket in the horizontal direction was high, implying that this movement of the racket was important. The value of wji of each marker in the vertical direction was low, meaning that the swing movement was stable and without rough wavy movements. In both horizontal and vertical directions after the third rule, the value of wji was constant and the swing was stable. On the other hand, the vertical rise and fall motion was a salient feature of beginners’ motion patterns and was extracted as the first rule. The horizontal movement at the shoulder and the elbow was remarkable in the second rule, implying “a movement to delay the body”. Figure 4 shows the first rule of the elite player and beginner. In this Figure, the difference between the elite player and the beginner is shown very conspicuously, making it easy to formulate table tennis skills as a rule.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.