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Simulation Training of E-Sports Players Based on Wireless Sensor Network

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Bibliographische Detailangaben
Zeitschriftentitel: Wireless Communications and Mobile Computing
Personen und Körperschaften: Qian, Feng
In: Wireless Communications and Mobile Computing, 2021, 2021, S. 1-10
Format: E-Article
Sprache: Englisch
veröffentlicht:
Hindawi Limited
Schlagwörter:
Electrical and Electronic Engineering
Computer Networks and Communications
Information Systems
author_facet Qian, Feng
Qian, Feng
author Qian, Feng
spellingShingle Qian, Feng
Wireless Communications and Mobile Computing
Simulation Training of E-Sports Players Based on Wireless Sensor Network
Electrical and Electronic Engineering
Computer Networks and Communications
Information Systems
author_sort qian, feng
spelling Qian, Feng 1530-8677 1530-8669 Hindawi Limited Electrical and Electronic Engineering Computer Networks and Communications Information Systems http://dx.doi.org/10.1155/2021/9636951 <jats:p>The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e-sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built-in signal processing function in the MR-XP 1.08 master edition software to perform full-wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e-sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> </jats:inline-formula>). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.</jats:p> Simulation Training of E-Sports Players Based on Wireless Sensor Network Wireless Communications and Mobile Computing
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source_id 49
title Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_unstemmed Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_full Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_fullStr Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_full_unstemmed Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_short Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_sort simulation training of e-sports players based on wireless sensor network
topic Electrical and Electronic Engineering
Computer Networks and Communications
Information Systems
url http://dx.doi.org/10.1155/2021/9636951
publishDate 2021
physical 1-10
description <jats:p>The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e-sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built-in signal processing function in the MR-XP 1.08 master edition software to perform full-wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e-sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> </jats:inline-formula>). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.</jats:p>
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description <jats:p>The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e-sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built-in signal processing function in the MR-XP 1.08 master edition software to perform full-wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e-sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> </jats:inline-formula>). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.</jats:p>
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spelling Qian, Feng 1530-8677 1530-8669 Hindawi Limited Electrical and Electronic Engineering Computer Networks and Communications Information Systems http://dx.doi.org/10.1155/2021/9636951 <jats:p>The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e-sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built-in signal processing function in the MR-XP 1.08 master edition software to perform full-wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e-sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (<jats:inline-formula> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> </jats:inline-formula>). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.</jats:p> Simulation Training of E-Sports Players Based on Wireless Sensor Network Wireless Communications and Mobile Computing
spellingShingle Qian, Feng, Wireless Communications and Mobile Computing, Simulation Training of E-Sports Players Based on Wireless Sensor Network, Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems
title Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_full Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_fullStr Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_full_unstemmed Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_short Simulation Training of E-Sports Players Based on Wireless Sensor Network
title_sort simulation training of e-sports players based on wireless sensor network
title_unstemmed Simulation Training of E-Sports Players Based on Wireless Sensor Network
topic Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems
url http://dx.doi.org/10.1155/2021/9636951