Sport skill classification using time series motion picture data

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

doi:10.1109/iecon.2014.7049304

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…In the sport domain, time series classification has been applied mostly for activity recognition in some specific sports such as table tennis (Maeda, Fujii, Hayashi, & Tasaka, 2014;Blank, Hoßbach, Schuldhaus, & Eskofier, 2015), and soccer (Hossain, Khan, & Roy, 2017). Hossain et al (2017) studied the use of wrist-worn sensors to classify motion performed by soccer field players such as passes, kicks, sprints and runs.…”

Section: Time Series Classificationmentioning

confidence: 99%

Time Series Data Mining for Sport Data: a Review

Komitova

Raabe

Rein

et al. 2022

International Journal of Computer Science in Sport

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Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.

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Section: Time Series Classificationmentioning

confidence: 99%

Time Series Data Mining for Sport Data: a Review

Komitova

Raabe

Rein

et al. 2022

International Journal of Computer Science in Sport

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“…Data collection from experts and novices for a motor skill could result in a classification of both groups based on movement differences [ 301 , 302 ]. This brings up the question of whether it would be possible to train the novices by giving them instructions that can reduce these differences.…”

Section: How To Transfer Motor Learning Principles To Complex Real Wo...mentioning

confidence: 99%

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Vandevoorde

Vollenkemper

Schwan

et al. 2022

Sensors

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Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

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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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Sport skill classification using time series motion picture data

Cited by 10 publications

References 3 publications

Time Series Data Mining for Sport Data: a Review

Time Series Data Mining for Sport Data: a Review

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

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

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