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Monitoring and prediction of exhaustion threshold during aerobic exercise based on physiological system using artificial neural network

Ahmad, Zulkifli and Jamaludin, Mohd. Najeb and Omar, Abdul Hafidz (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. 1-3. ISSN 2577-2945

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Official URL: http://dx.doi.org/10.19080/JPFMTS.2018.03.555624

Abstract

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.

Item Type:Article
Uncontrolled Keywords:aerobic exercise, exhaustion, wearable, machine learning method
Subjects:Q Science > Q Science (General)
Q Science > QH Natural history > QH301 Biology
Divisions:Biosciences and Medical Engineering
ID Code:81981
Deposited By: Yanti Mohd Shah
Deposited On:30 Sep 2019 17:00
Last Modified:08 Oct 2019 17:12

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