Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network
Zulkifli Ahmad1,2*, Najeb Jamaludin1 and Abdul Hafidz Omar1
1Faculty of Biosciences and Medical Engineering, Universiti Teknologi, Malaysia
2Faculty of Mechanical Engineering, Universiti Malaysia Pahang, Malaysia
Submission: May 07, 2018;Published: May 16, 2018
*Corresponding author: Zulkifli Ahmad, Institute of Human Centered Engineering, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia, Tel: +6094246286; Email: email@example.com
How to cite this article: Zulkifli Ahmad, Najeb Jamaludin, Abdul Hafidz Omar. Monitoring and Prediction of Exhaustion Threshold during Aerobic Exercise Based on Physiological System using Artificial Neural Network. J Phy Fit Treatment & Sports. 2018; 3(5): 555624. DOI: 10.19080/JPFMTS.2018.03.555624
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.
Movements in sports are the main criteria of the physical exercise, and it is a combination of muscle contraction and the human skeleton which reflected in musculoskeletal systems. However, the most affected movements were muscle activation due to aerobic exercise such as walking; jumping and running required a repetitive motion. Muscle contraction during aerobic exercise is related to the oxygen consumption in order to endure the repetition of movement in a period of time. Therefore, it depends on the demand and supply by the muscle and oxygen respectively. Based on the blood circulation in the systemic and pulmonary system, oxygen (O2) was exchanged with carbon dioxide (CO2) at two places; in the respiratory system by alveolus membrane tissue  and bundle of muscle tissue. These organs were functioned to ensure that volume of oxygen capacity in the physiological is equal to remain the balance condition. Fatigue is a maximum condition of exercise when the level of oxygen was decreased while the lactic acid produced greater. This lactic
acid production will reduce the ability of muscle to contract as performing the physical exercise .
Monitoring the level of fatigue by focused to the muscle contraction based on the oxygen consumption not represent the whole physiological activity during aerobic exercise. It is significant to consider another system into account for the measurement such as respiratory and cardiovascular systems. Hence, there are some previous researchers were associated fatigue or exercise to the heart rate variability [3,4], blood pressure , respiratory rate , electromyogram [7,8], and mechanomyogram . On the other hand, there has been in recent years, an increasing amount of literature on detecting fatigue by using the EMG monitoring with signal processing analysis [10-13], computer control  and statistical . However, in terms of oxygen circulation to supply to working muscle, the other physiological systems such as respiratory and cardiovascular or called as cardio respiratory systems are corresponded to the physical exercise . Therefore, physical
exhaustion is not exclusively due to weakness of muscle, but
it can be affected to the other parameters as well. Additional
consideration of parameters into account will help to investigated
exhaustion in wide angle of views and details.
Traditionally, the physical exhaustion is related to the
muscle fatigue. However, combination of input parameters or
data fusion in identified exhaustion is lacking in the previous
research due to enlarging its uncertainty. Therefore, this
proposed study objectively to predict an exhaustion level by
reducing its uncertainty through the machine learning method.
This approach was hypothesized that exhaustion is reflected in
the blood or oxygen circulation rate during aerobic exercise. By
using machine learning method, it will predict accurately the
level of exhaustion based on the threshold. Consequently, this
approach beneficial to the athlete and coach to monitoring their
level of exhaustion and prevent from the maximum condition
that can be led to severe injury.
Overall structure of this proposed study was designed
in the Figure 1 including the theory and procedure that will
be conducted to accomplish the objective. It illustrates the
parameters involved in monitoring fatigue based on selected
physiological measurement using the wearable device. Three
parameters identified as a main contribution in this study, which
is respiratory rate (RR), heart rate (HR) and electromyogram
(EMG). It also represents for the three physiological systems
considered in blood circulation, which indicates oxygen
consumption by the muscle contraction. A wearable device of
each measurement was developed to monitor and detect fatigue
by providing an alarm to warn the user during performing an
exercise. There are five (5) indicators that differ from their level
of fatigue initiated with status very light and end to the maximum
intensity. Since the fatigue level of each person varies due to
human uncertainty, a set of data during fatigue as a threshold
is needed to categorize based on control group criteria such as
BMI, ages, and sports.
By using the machine learning method, it could be a good
way to predict the level of fatigue in supervised approach.
This technique is believed to be an objectively assessment by
mathematical model to the response promptly and reduce the
time consumption. It was proved when applied for localized
muscle fatigue , hand grasp movements , predict sudden
cardiac death based on heart rate variability , and estimating
upper extremity movements .
On the other hand, wearable device functioned to evaluating
exhaustion by those selected parameters in analyzing data
collection. Therefore, Figure 2 indicates the schematic diagram
for the data analysis processes in predicting exhaustion zones
in real-time monitoring. It has three stages; 1) pre- processing,
2) post-processing and 3) displaying exhaustion zones. In preprocessing
stage, three signals from three different signals
were measured, which are respiratory rate, heart rate, and
electromyogram. The raw signals filtered and removed the
artefacts that interrupting the real signals by using signal
processing method. These measurements were then collected
and stored in the temporary database prior to move to the
next stage for analysis. In this level as well, the databases were
kept in a statistical formatting in order to make it easier for the
feature extraction procedure. Next stage is the post-processing
procedure for the analysis using the machine learning method
which is artificial neural network (ANN). There is another
approach that can be used such as support vector machine (SVM)
and Multilinear Regression (MR) to predict and accomplished
this purpose, but it was just about the result’s accuracy. Finally,
the results from the machine learning method will present the
prediction of the exhaustion level by indicating based on status
of zones range over sequence from very light to the maximum.
In addition, this method for developing an algorithm was
translated into the mathematical model that can be integrated
with the current wearable device for the real-time monitoring
assessment. For the future recommendation, this device will
be upgraded with the intelligent user interface mechanism to
be incorporated with the Android apps on the smart phone.
Furthermore, visual tools were contained for the output alarm
to alert the user such as color of LED and tone of the buzzer.
All of these implements will enhance the wearable device to the
smart operating function.
This study is promising to monitor and predict an exhaustion
threshold by using the wearable device incorporating machine
learning method. The problem of the vigorous movements
among the athlete or sports can be solved by implemented this
algorithm in terms of signal processing. Meanwhile, this small
size of device attached to the body tightly will not bothering
their movement on the field.
The Authors would like to acknowledge Universiti Teknologi
Malaysia (UTM) as supported funding under the Research
University Grant, Tier 2 (Q.J130000.2645.13J73). Also to
Ministry of Higher-Education Malaysia for sponsoring the PhD
study in UTM.