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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.


Position of markers; observed racket swing positions expressed in the (x, y) directions.
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j_hukin-2017-0005_fig_001: Position of markers; observed racket swing positions expressed in the (x, y) directions.

Mentions: A 90 fps camera was used to record subjects for 10 min what resulted in 54000 recorded frames. From this data, image sequences of 40 to 120 frames were extracted. During each 10 min recording, the subject continued swinging the racket for 100 to 150 times. Overall, the trajectories generated by the elite subjects were very similar across swings. However, data from the elite trajectories could not be easily compared to that of the beginner and middle-level subjects due to variations in the start times in the latter’s trajectories. In addition, the last swing trajectories in the 10 min periods were often unreliable because of a lack of concentration at that time. Therefore, we used trajectory data for the middle portion of the 10 min recordings, e.g., in a 9 min 20 s recording, we took the swing at 280 s (9 min 20 s = 560 s, divided by two equals 280 s). In addition, the start time of the racket swing was extracted from the time when the position of the take-back was minimized in the x direction to the time when the position of the follow-through became greatest in the x direction. Therefore, from one experimental sequence consisting of swing movements for ten minutes, it was possible to extract between 40 and 120 frames of data. As a result, the number of training data points for seven subjects was approximately 600 and the number of testing data points for two subjects was approximately 200. In each image frame, two-dimensional ( x, y) coordinates of nine measurement markers relative to the original position of the subject’s shoulder on the first frame were obtained. The observed positions of markers are presented in Figure 1 and the speed of the horizontal direction (x) in Figure 2.


Extraction of Knowledge from the Topographic Attentive Mapping Network and its Application in Skill Analysis of Table Tennis
Position of markers; observed racket swing positions expressed in the (x, y) directions.
© Copyright Policy
Related In: Results  -  Collection

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

j_hukin-2017-0005_fig_001: Position of markers; observed racket swing positions expressed in the (x, y) directions.
Mentions: A 90 fps camera was used to record subjects for 10 min what resulted in 54000 recorded frames. From this data, image sequences of 40 to 120 frames were extracted. During each 10 min recording, the subject continued swinging the racket for 100 to 150 times. Overall, the trajectories generated by the elite subjects were very similar across swings. However, data from the elite trajectories could not be easily compared to that of the beginner and middle-level subjects due to variations in the start times in the latter’s trajectories. In addition, the last swing trajectories in the 10 min periods were often unreliable because of a lack of concentration at that time. Therefore, we used trajectory data for the middle portion of the 10 min recordings, e.g., in a 9 min 20 s recording, we took the swing at 280 s (9 min 20 s = 560 s, divided by two equals 280 s). In addition, the start time of the racket swing was extracted from the time when the position of the take-back was minimized in the x direction to the time when the position of the follow-through became greatest in the x direction. Therefore, from one experimental sequence consisting of swing movements for ten minutes, it was possible to extract between 40 and 120 frames of data. As a result, the number of training data points for seven subjects was approximately 600 and the number of testing data points for two subjects was approximately 200. In each image frame, two-dimensional ( x, y) coordinates of nine measurement markers relative to the original position of the subject’s shoulder on the first frame were obtained. The observed positions of markers are presented in Figure 1 and the speed of the horizontal direction (x) in Figure 2.

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.