Acquisition of embodied knowledge on sport skill using TAM network

Hayashi, Isao; Maeda, Toshiyuki; Fujii, Masanori; Wang, Shuoyu; Tasaka, Tokio

doi:10.1109/fuzzy.2009.5277228

Cited by 5 publications

(1 citation statement)

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“…Therefore, it cannot be used to provide visualizations that would be useful to players. In our approach, we extract input attributes and skill rules of the forehand stroke of table tennis with the TAM network as an internal model ( Hayashi et al, 2009 ; Maeda et al, 2014 ). The purpose of analyzing movement data with a TAM network is to visualize, for example, knowledge in the form of rules extracted from movement data.…”

Section: Introductionmentioning

confidence: 99%

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

Hayashi

Fujii

Maeda

et al. 2017

Journal of Human Kinetics

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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.

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Section: Introductionmentioning

confidence: 99%