Cargando…

Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network

Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert...

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Wen-Lan, Liang, Jing-Min, Chen, Chien-Fei, Tsai, Kuei-Lan, Chen, Nian-Shing, Lin, Kuo-Chin, Huang, Ing-Jer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200036/
https://www.ncbi.nlm.nih.gov/pubmed/34205215
http://dx.doi.org/10.3390/s21113870
_version_ 1783707516708847616
author Wu, Wen-Lan
Liang, Jing-Min
Chen, Chien-Fei
Tsai, Kuei-Lan
Chen, Nian-Shing
Lin, Kuo-Chin
Huang, Ing-Jer
author_facet Wu, Wen-Lan
Liang, Jing-Min
Chen, Chien-Fei
Tsai, Kuei-Lan
Chen, Nian-Shing
Lin, Kuo-Chin
Huang, Ing-Jer
author_sort Wu, Wen-Lan
collection PubMed
description Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. Results: The overall model’s recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills “explosive force of the forearm” and “wrist muscle control” were extracted, and their factor scores were standardized (0–100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. Conclusion: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills.
format Online
Article
Text
id pubmed-8200036
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-82000362021-06-14 Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network Wu, Wen-Lan Liang, Jing-Min Chen, Chien-Fei Tsai, Kuei-Lan Chen, Nian-Shing Lin, Kuo-Chin Huang, Ing-Jer Sensors (Basel) Communication Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. Results: The overall model’s recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills “explosive force of the forearm” and “wrist muscle control” were extracted, and their factor scores were standardized (0–100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. Conclusion: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills. MDPI 2021-06-03 /pmc/articles/PMC8200036/ /pubmed/34205215 http://dx.doi.org/10.3390/s21113870 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Wu, Wen-Lan
Liang, Jing-Min
Chen, Chien-Fei
Tsai, Kuei-Lan
Chen, Nian-Shing
Lin, Kuo-Chin
Huang, Ing-Jer
Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title_full Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title_fullStr Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title_full_unstemmed Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title_short Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network
title_sort creating a scoring system with an armband wearable device for table tennis forehand loop training: combined use of the principal component analysis and artificial neural network
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200036/
https://www.ncbi.nlm.nih.gov/pubmed/34205215
http://dx.doi.org/10.3390/s21113870
work_keys_str_mv AT wuwenlan creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT liangjingmin creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT chenchienfei creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT tsaikueilan creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT chennianshing creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT linkuochin creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork
AT huangingjer creatingascoringsystemwithanarmbandwearabledevicefortabletennisforehandlooptrainingcombineduseoftheprincipalcomponentanalysisandartificialneuralnetwork