Analysis of effect of exercise on EMG in MATLAB
Ayesha Ijaz
dept. Biomedical engineering
Air University
Islamabad, Pakistan
231896@students.au.edu.pk
Maryam Najeeb
dept. Biomedical engineering
Air University
Islamabad, Pakistan
231910@students.au.edu.pk
Fatimah Waseem
dept. Biomedical engineering
Air University
Islamabad, Pakistan
231915@students.au.edu.pk
Abdullah Raees
dept. Biomedical engineering
Air university
Islamabad, Pakistan
233045@students.au.edu.pk
Saim Khursheed
dept. Biomedical engineering
Air university
Islamabad, Pakistan
231913@students.au.edu.pk
phase responses efficiently.[4]
Problem Statement--- Exercise-induced changes in muscle
activity are reflected in EMG signals, but their analysis is often
complicated by noise and motion artifacts. These unwanted
components can mask crucial information, making it difficult to
accurately interpret physiological responses. The challenge is
to develop a robust signal processing pipeline in MATLAB that
effectively filters out noise while preserving the integrity of the
muscle activation signals. This project addresses the need for
reliable EMG signal processing to analyze muscle behavior
under varying levels of physical exertion, thereby enhancing
understanding of muscle fatigue and recovery patterns [1],[2].
Abstract— This project explores the physiological impact
of exercise on the human body by analyzing electromyogram
(EMG) signals. Utilizing MATLAB, we created a detailed
code to plot and compare these bioelectrical signals across
three conditions: at rest (baseline), following mild exercise,
and after intense exercise. The main goal is to visualize and
interpret how different levels of physical activity affect muscle
function. By studying these changes, we aim to gain a better
understanding of muscle fatigue, activity patterns, and the
overall physiological response to exercise. The generated
plots clearly illustrate signal variations, providing valuable
insights into muscular behavior during physical exertion.
Keywords—EMG, exercise, MATLAB, muscle fatigue,
physiological response, physical exertion.
I.
INTRODUCTION
MATLAB, a high-level programming environment by
MathWorks, was the primary tool used in this experiment.
MATLAB is a programming and numeric computing platform
for engineering and scientific applications like data analysis,
signal and image processing, control systems, wireless
communications, and robotics [1]. MATLAB is a highperformance language for technical computing. It integrates
computation, visualization, and programming in an easy-to-use
environment where problems and solutions are expressed in
familiar mathematical notation.[2]. It offers powerful support
for signal processing through built-in functions and toolboxes.
MATLAB was used to design, simulate, and analyze IIR filters,
as well as to generate test signals such as sine waves, noise, and
composite inputs for evaluating filter performance. Its plotting
capabilities allowed for detailed comparison of time-domain
waveforms and their frequency spectra before and after filtering.
MATLAB also provides versatile capabilities for signal
generation and visualization, allowing users to create synthetic
signals, simulate filter behavior, and compare frequency and
Electromyography (EMG) is a diagnostic procedure
to assess the health of muscles and the nerve cells that
control them (motor neurons). EMG results can reveal
nerve dysfunction, muscle dysfunction or problems
with nerve-to-muscle signal transmission.
Motor neurons transmit electrical signals that cause
muscles to contract. An EMG uses tiny devices called
electrodes to translate these signals into graphs, sounds
or numerical values that are then interpreted by a
specialist [5].
II.
MATERIALS AND METHOD
a) Materials
Following are the tools that are used in this activity:
 MATLAB Software
b) Method:
1. Data Acquisition
The data is taken from the UCI Machine Learning
Repository [6] site is based on the study of
participants who performed some physical activities
and then there EMG signals were recorded. The data
was recorded at rest, after mild exercise, after extreme
exercise. The recorded EMG signal is effect of various
physical activities on EMG at different muscles such
as biceps femoris and rectus femoris.So the data that is
used in this activity is further processed.
2. Data Analysis
To analyze the data MATLAB is used. All of the
analysis is done through coding in MATLAB:

After downloading the data , the zip file is
extracted. After performing the extraction the data
was to be imported on MATLAB.

Further various changes are to carried out ; for
example; loading the data onto MATLAB,
removing noise, applying filter and take the FFT,
plot the comparison in EMG at different physical
activities.

Pre-Processing: Applying noise reduction
techniques to enhance signal quality.

Filtering: Implementing tailored filtering
methods to remove baseline wander, motion
artifacts, and other noise components. Critical
parameters such as filter order, cutoff frequency,
and filter type are carefully tuned to maintain a
balance between noise suppression and signal
preservation [3].

Visualization: Plotting the filtered signals to
compare variations under different physical
conditions [7].
III.
RESULTS AND DISCUSSION
Filtering significantly improves signal quality, allowing for a
clearer analysis of muscle activation and fatigue patterns. By
selectively attenuating noise frequencies while preserving
physiological activity, the filtration ensures the accuracy of
subsequent data interpretation. The enhanced signals reveal
notable differences in EMG patterns between rest and postexercise conditions.
IV.
CONCLUSION
Through this project, we demonstrate the importance of
effective signal processing in biomedical applications. By
integrating filtering techniques into EMG signal analysis, we
achieve more reliable and interpretable data, fostering a deeper
understanding of how exercise influences muscle function. The
insights gained from the filtered EMG signals offer potential
applications in diagnostic procedures and biomedical research.
REFERENCES
[1] J. Smith, “Biomedical Signal Processing: Techniques and
Applications,” IEEE Trans. Biomed. Eng., vol. 65, no. 5, pp.
1234–1245, 2024.
[2] M. Ahmad, S. Khan, and F. Rehman, “Analysis of EMG
Signals during Physical Activity: Noise Reduction and Feature
Extraction,” IEEE Access, vol. 11, pp. 4590–4601, 2025.
[3] L. Zhang and H. Wang, “Advanced Filtering Techniques
for Electromyography Signal Processing in MATLAB,” IEEE
Trans. Instrum. Meas., vol. 74, no. 3, pp. 1023–1031, 2024.
[4] MathWorks, "What is MATLAB?" MathWorks, 2025.
[Online]. Available:
https://www.mathworks.com/solutions/matlab.html.
[5] Mayo Clinic Staff, "Electromyography (EMG)," Mayo
Clinic, 2025. [Online]. Available:
https://www.mayoclinic.org/tests-procedures/emg/about/pac20393913.
[6] EMG dataset in Lower Limb - UCI Machine Learning
Repository
[7] A. Brown, “MATLAB for Biomedical Signal Analysis,”
Proc. IEEE Int. Conf. Biomed. Eng., pp. 567–570, 2023.