



























57 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

Conference Paper

Human Muscle State Machine Using Electromyography 
Classification with Machine Learning 

George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2 and Panagiotis D. Bamidis1 

1 Lab of Medical Physics & Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 
Thessaloniki, Greece. 
2 Laboratory of Fluid Mechanics and Turbomachinery, Department of Mechanical Engineering, Aristotle University of Thessaloniki (AUTH), 
Thessaloniki, Greece. 

* Corresponding Author Email: georgios.lyssas@gmail.com 

ABSTRACT

This research aims to create a tool that can recognize the state of the human skeletal muscle from surface Electromyography 
(sEMG) signals. The goal of this muscle state machine is for use in the functional rehabilitation of people suffering from Spinal 
cord injury and for stroke survivors who have lost mobility in their upper body limbs. The use of machine learning techniques 
for the classification of these muscle states brought forth the need for database creation to train the generated ML model. For 
the data collection process, an experimental protocol was proposed, and tests were conducted in healthy individuals with a 
Nexus MKII medical device. Following the data collection, a signal analysis procedure was performed to extract features from 
the sEMG signals that directly relate to the muscle state. In addition to the signal analysis, a Machine Learning classification 
model was created to recognize and classify the sEMG signals in different states of the muscle. This classification had a high 
enough accuracy of producing the correct result, given that the training and sampling size of the database was considerably 
small provided that in similar cases of ML classifying models the size of the Databases includes way more samples than the 
one in this research. The future steps for this research are the creation of a more extensive and diverse database and using this 
model in real-time situations. 

Keywords—Electromyography, Machine learning, Signal classification. 

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

http://www.globalce.org
http://globalce.org
http://globalce.org
mailto:georgios.lyssas@gmail.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/


J Global Clinical Engineering Vol.6 Special Issue 6: 2024 58

INTRODUCTION

People who suffer from Spinal Cord Injury or are stroke 
survivors experience a loss in their mobility as an after-
effect of their condition.1 To combat these aftereffects, 
functional rehabilitation is used. In cases of movement 
loss, the activation of the muscles is not visible in most 
cases, this is a problem that is impacting rehabilitation 
practitioners and a solution needs to be proposed. In 
this research, a solution to this problem was explored 
with the creation of a Machine Learning model that is 
trained to recognize and categorize the Electromyogra-
phy signals that are produced from the activation of the 
skeletal muscles. This model simulates a state machine 
of the human skeletal muscle and the states recognized 
were the state of no activation, the activation state, and 
the muscle fatigue state. These states were selected due 
to their high importance in the procedure of patient reha-
bilitation. To train the model for recognizing the muscle 
states an extensive database of electromyography signals 
must be produced.2 

MATERIALS AND METHODS 

In this research there were no open-source databases 
that were relevant to the recognition of the muscle states, 
therefore the creation of such a database was imminent. 
For the creation of a database, a measurement protocol 
was made and introduced in this research with the scope 
of measuring the states of the human skeletal muscle. 
This protocol targeted the muscles of the upper extremi-
ties specifically the bicep and triceps muscles of both 
arms as it is illustrated in Figures 1 and 2. The exercises 
introduced were a set of 10 isometric contractions of the 
muscle without any external weight for measuring the 
baseline activation of the muscle, a continuous maximum 
contraction of the muscle that was held for 10 seconds so 
that the maximum contraction signal could be measured 
and lastly, a set of isometric contractions with an external 
weight of 5 kg until the subject was unable to continue, 
which was the start of muscle fatigue. In the last set of 
exercises, the goal was to have an electromyography sig-
nal that included all the muscle states and the transition 
between those states. Those exercises were performed 
for both the bicep and tricep muscles of both arms of the 
subjects. The number of subjects that participated in this 

procedure was 20. 13 of which were male and 7 were 
female with a mean age of 27, and the subjects that were 
a part of this research were selected. The measurements 
were recorded by a Nexus MKII medical device, and the 
files of the measurements were later extracted for a signal 
processing sequence that created the final database for 
training the machine learning model.3

After the extraction of the files of the measurements 
the procedure followed is shown in Figure 3, the timeline 
of the signal is in the form of a Raw-EMG signal, this form 
includes noise from the recording and measuring process 
and it is imminent to denoise the signal, for the features 
and the information of the signal to be clear and readable, 
the creation of the envelope of the signal was the result of 

FIGURE 1. Measurement protocol. 

FIGURE 2. Example of measurement.

http://www.globalce.org
http://globalce.org
http://globalce.org


59 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

the denoising procedure. Having completed the denoising 
procedure the signal was later separated into parts that 
contained an event in the timeline of the signal; those 
parts are referred to as epochs and are the data points of 
the database that was created. From the epochs created 
features of high importance to the classification of muscle 
states were extracted, those features were based on the 
time and frequency domain and parameters of the signal 
shape. Those parameters were the mean and median 
frequency, the mean Amplitude, the Hjorth Parameters 
(activity, mobility, complexity), the skewness and kurtosis 
of the signal, and also the Continuous Wavelet Transform 
of the epochs.4–6 The features underwent a power analy-
sis the results of which can be seen in Figure 4. All those 
features were later introduced to the Machine Learning 
model for the creation of the classifier (Figure 4). 

After the creation of the database three machine 
learning techniques were implemented and compared. 

The techniques used were a Random Forest classifier7 
model (a representation of a tree can be seen in Figure 
5), an SVM model, and a Shallow Neural Network, which 
were created with Python programming language and 
with the use of the SciKit Learn and Tensorflow Keras 
libraries. Those models were trained with the created 
Database and their speed and reliability in their results 
were measured so that the optimal between the three 
models could be chosen as the Muscle state machine.8 

RESULTS AND DISCUSSION 

For the comparison between the efficiency and 
reliability of the machine learning models the use of 
metrics is the discerning factor between those models. 
The metrics that were used can be seen in Figure 6 where 
it is apparent that The Random Forest classifier model 
achieved the highest score among the other models. 

DISCUSSION
In conclusion, the reliability and the adaptability of the 

Random Forest model is the best choice for the recogni-
tion and classification of EMG signals to muscle states, in 
this application with a small sample size. Although these 
results show that the Random Forest model is the best 
choice among the other models the effective database for 
the training of these models was quite limited and the 
accuracy with which those models recognize the muscle 
states is certainly influenced. This leads to the need for 
the creation of a completed and reliable Electromyography 
signals database to further encourage the use of ML in 
Biomedical applications. 

FIGURE 3. Signal analysis procedure.

FIGURE 4. Signal features power analysis. 

http://www.globalce.org
http://globalce.org
http://globalce.org


J Global Clinical Engineering Vol.6 Special Issue 6: 2024 60

ACKNOWLEDGMENTS 
This study was conducted as a part of the research 

program “NeuroSuitUp: Neurorehabilitation through 
synergistic man-machine interfaces promoting dormant 
neuroplasticity in spinal cord injury” (MIS 5047840) 
and will be implemented and clinically validated in 
the context of the project “HEROES: Human Extremity 
Robotic Rehabilitation and Outcome Enhancement for 
Stroke funded by H.F.R.I.” Special thanks to Dr. Alkinoos 
Athanasiou, Alexander Astaras, Athanasios Arvanitidis, 
Niki Pandria, and Vasileia Petronikolou. 

REFERENCES

1. Athanasiou, A., Mitsopoulos, K., Praftsiotis, A., et al. 
Neurorehabilitation Through Synergistic Man Ma-
chine Interfaces Promoting Dormant Neuroplasticity 
in Spinal Cord Injury: Protocol for a Nonrandomized 
Controlled Trial. JMIR Res Protoc. 2022;11(9):e41152. 
https://doi.org/10.2196/41152. 

2. Let, A.M., Filip, V., Let, D., et al. A Review in Biomechanics 
Modeling. In Proceedings of the International Conference 
of Mechatronics and Cyber—MixMechatronics—2020. 
Gheorghe G.I., eds. Springer International Publishing: 

FIGURE 5. Visualization example of a random forest tree.

FIGURE 6. Result of comparison with metrics (closer to 100% is better). 

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.2196/41152


61 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

Zurich, Switzerland; 10–11 September 2020; pp.156–164. 
https://doi.org/10.1007/978-3-030-53973-3_17. 

3. Briouza, S., Gritli, H., Khraief, N., et al. Classification of 
sEMG Biomedical Signals for Upper-Limb Rehabilita-
tion Using the Random Forest Method. In 2022 5th 
International Conference on Advanced Systems and 
Emergent Technologies (IC_ASET). IEEE Xplore: 22–25 
March 2022; pp. 161–166. https://doi.org/10.1109/
IC_ASET53395.2022.9765871. 

4. Cifrek, M., Medved, V., Tonković, S., et al. Surface EMG 
based muscle fatigue evaluation in biomechanics. 
Clin Biomech. 2009;24(4):327–340. https://doi.
org/10.1016/j.clinbiomech.2009.01.010. 

5. Karthick, P.A. and Ramakrishnan, S. Analysis of 
fatigue conditions in biceps brachii muscles using 
surface electromyography signals and strip spectral 
correlation. In 2014 19th International Conference 
on Digital Signal Processing. IEEE Xplore: 20–23 Au-
gust 2014; pp. 190–194. https://doi.org/10.1109/
ICDSP.2014.6900826. 

6. Rangayyan, R.M. Biomedical Signal Analysis. 2nd ed. 
Wiley-IEEE Press: Hoboken, NJ, USA; 2015. 

7. Gokgoz, E. and Subasi, A. Comparison of decision tree 
algorithms for EMG signal classification using DWT. 
Biomed Signal Process Control. 2015;18:138–144. 
https://doi.org/10.1016/j.bspc.2014.12.005. 

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.1007/978-3-030-53973-3_17
https://doi.org/10.1109/IC_ASET53395.2022.9765871
https://doi.org/10.1109/IC_ASET53395.2022.9765871
https://doi.org/10.1016/j.clinbiomech.2009.01.010
https://doi.org/10.1016/j.clinbiomech.2009.01.010
https://doi.org/10.1109/ICDSP.2014.6900826
https://doi.org/10.1109/ICDSP.2014.6900826
https://doi.org/10.1016/j.bspc.2014.12.005

	Editor’s Corner
	Biomedical Technology and Clinical Engineering in Greece after the Pandemic: Highlighted Works from the Panhellenic Conference of Biomedical Technology
	Aris Dermitzakis1,2,*, Vasiliki Zilidou1,3, Eleftheria Vellidou1,4, Alkinoos Athanasiou1,3

	Digital Transformation Management in Health Services: Health Professionals Perceptions as an Implementation Factor
	Theodoros S. Tanis*, Chryssoula Chatzigeorgiou, Ioanna Simeli, and Evangelia Stalika

	Validating the ID-GAMING e-Training Toolkit for People with Intellectual Disabilities in Greece
	Niki Pandria*, Anastasia Barboudi, Vasileia Petronikolou, Panagiotis Antoniou and Panagiotis D. Bamidis

	Novel Functional Electrical Stimulation Parameter Optimization for Neurorehabilitation Using Both Conventional and AI Techniques 
	Arsenios Arsenidis1, Alexandros Moraitopoulos2, Alkinoos Athanasiou2, Alexandros Vildiridis3, Panagiotis Bamidis2, Petros Stefaneas4 and Alexandros Astaras5 

	Leveraging Web Scraping and API Integration for Efficient Medical Device Data Management 
	Agapi Konstantina Liontou1,*, Spilios Zisimopoulos2 and Aris Dermitzakis1 

	Human Muscle State Machine Using Electromyography Classification with Machine Learning 
	George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2, Panagiotis D. Bamidis1 

	Kinematic and Dynamic Analysis of Lower Limb Movement: Towards the Design of a Wearable Rehabilitation Assistant Device 
	Filippos Margaritis1,*, Konstantinos Mitsopoulos1, Kostas Nizamis2, Alkinoos Athanasiou1 and Panagiotis D. Bamidis1 

	A Novel Dermatological Diagnosis Support Device Based on Electrical Impedance Spectroscopy 
	Alexandros Moraitopoulos1,*, Konstantinos Mitsopoulos1, Christina Kemanetzi2, Panagiotis Bamidis1 and Alexandros Astaras3

	Software Skills Identification: A Multi-Class Classification on Source Code Using Machine Learning
	Dimitris Bamidis, Ilias Kalouptsoglou, Apostolos Ampatzoglou, Alexandros Chatzigeorgiou*

	Improvement of Aortic Valve Stenosis Classification in Patients Through Computational Fluid Dynamics Model
	Ioannis Makropoulos, Dimitris Zantzas, Vasilis Gkoutzamanis, Anestis Kalfas*

	Kinematic and Dynamic Analysis of the Human Hand’s Articulation for Wearable Soft-Robotic Device Applications
	Paschalina-Danai Sarra, Vasiliki Fiska, Konstantinos Mitsopoulos, Diamanto Mylopoulou, and Panagiotis D. Bamidis*

	Deep Learning Classification of Epileptic Magnetoencephalogram 
	Andreas Stylianou1, Lefteris Koumakis2, Maria Hadjinicolaou3, Adam Adamopoulos1,* and Alkinoos Athanasiou4


