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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| Format: | Article |
| Language: | English |
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Juniper Publishers
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30686/ http://umpir.ump.edu.my/id/eprint/30686/7/Monitoring%20and%20Prediction%20of%20Exhaustion%20Threshold%20during%20Aerobic%20Exercise%20Based.pdf |
| _version_ | 1848823575893508096 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:59:19Z |
| format | Article |
| id | ump-30686 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:59:19Z |
| publishDate | 2018 |
| publisher | Juniper Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. (Published) https://dx.doi.org/10.19080/JPFMTS.2018.03.555624 |
| 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 |
| title | 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_short | 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 |
| topic | QP Physiology TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/30686/ http://umpir.ump.edu.my/id/eprint/30686/ http://umpir.ump.edu.my/id/eprint/30686/7/Monitoring%20and%20Prediction%20of%20Exhaustion%20Threshold%20during%20Aerobic%20Exercise%20Based.pdf |