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Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network

Exhaustion or extreme of fatigue is the highest condition of body performance during exercise. This state presents an optimum energy to execute by an athlete before their level of fitness reduced and required the recovery process. The purpose of this study is to monitor and predict an exhaustion thr...

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Main Authors: Zulkifli, Ahmad@Manap, Mohd Najeb, Jamaludin, Abdul Hafidz, Omar
Format: Article
Language:English
Published: Juniper Publishers 2018
Subjects:
QP Physiology
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/30686/7/Monitoring%20and%20Prediction%20of%20Exhaustion%20Threshold%20during%20Aerobic%20Exercise%20Based.pdf
http://umpir.ump.edu.my/id/eprint/30686/
https://dx.doi.org/10.19080/JPFMTS.2018.03.555624
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spelling my.ump.umpir.306862021-02-18T08:51:36Z http://umpir.ump.edu.my/id/eprint/30686/ Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network Zulkifli, Ahmad@Manap Mohd Najeb, Jamaludin Abdul Hafidz, Omar QP Physiology TJ Mechanical engineering and machinery Exhaustion or extreme of fatigue is the highest condition of body performance during exercise. This state presents an optimum energy to execute by an athlete before their level of fitness reduced and required the recovery process. The purpose of this study is to monitor and predict an exhaustion threshold from three physiological systems; respiratory, cardiovascular and muscular by using artificial neural network. A developed wearable device to measure those parameters is needed for the data collection in fatigue experiment protocol. Then, it was separated into its category and filtering that signal to remove all unwanted noise in the database. Statistical feature extraction was executed for divided into five levels of exhaustion to implement supervised machine learning method. A mathematical model for prediction was developed in artificial neural network based on the data obtained from the exhaustion threshold. This model can facilitate the coach and athlete to monitor their level of exhaustion as well as prevent from the severe injury due to over exercise. Juniper Publishers 2018-05 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30686/7/Monitoring%20and%20Prediction%20of%20Exhaustion%20Threshold%20during%20Aerobic%20Exercise%20Based.pdf Zulkifli, Ahmad@Manap and Mohd Najeb, Jamaludin and Abdul Hafidz, Omar (2018) Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network. Journal of Physical Fitness, Medicine & Treatment in Sports., 3 (5). pp. 1-4. ISSN 2577-2945 https://dx.doi.org/10.19080/JPFMTS.2018.03.555624
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QP Physiology
TJ Mechanical engineering and machinery
spellingShingle QP Physiology
TJ Mechanical engineering and machinery
Zulkifli, Ahmad@Manap
Mohd Najeb, Jamaludin
Abdul Hafidz, Omar
Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
description Exhaustion or extreme of fatigue is the highest condition of body performance during exercise. This state presents an optimum energy to execute by an athlete before their level of fitness reduced and required the recovery process. The purpose of this study is to monitor and predict an exhaustion threshold from three physiological systems; respiratory, cardiovascular and muscular by using artificial neural network. A developed wearable device to measure those parameters is needed for the data collection in fatigue experiment protocol. Then, it was separated into its category and filtering that signal to remove all unwanted noise in the database. Statistical feature extraction was executed for divided into five levels of exhaustion to implement supervised machine learning method. A mathematical model for prediction was developed in artificial neural network based on the data obtained from the exhaustion threshold. This model can facilitate the coach and athlete to monitor their level of exhaustion as well as prevent from the severe injury due to over exercise.
format Article
author Zulkifli, Ahmad@Manap
Mohd Najeb, Jamaludin
Abdul Hafidz, Omar
author_facet Zulkifli, Ahmad@Manap
Mohd Najeb, Jamaludin
Abdul Hafidz, Omar
author_sort Zulkifli, Ahmad@Manap
title Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
title_short Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
title_full Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
title_fullStr Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
title_full_unstemmed Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
title_sort monitoring and prediction of exhaustion threshold during aerobic exercise based on physiological system using artificial neural network
publisher Juniper Publishers
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/30686/7/Monitoring%20and%20Prediction%20of%20Exhaustion%20Threshold%20during%20Aerobic%20Exercise%20Based.pdf
http://umpir.ump.edu.my/id/eprint/30686/
https://dx.doi.org/10.19080/JPFMTS.2018.03.555624
_version_ 1692991966642438144
score 13.211869

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