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Conference Paper

Design of A Normative sEMG Database for Biometric 
Comparison in Rehabilitation Research 

Athanasios Arvanitidis*, Konstantinos Mitsopoulos, Vasiliki Fiska, Alkinoos Athanasiou and Panagiotis D. Bamidis 

Aristotle University of Thessaloniki, School of Medicine, Lab of Medical Physics and Digital Innovation, Thessaloniki, Greece. 

* Corresponding Author Email: thanosarv99@gmail.com 

ABSTRACT

Electromyography (EMG) is used in a wide range of research fields, such as physiotherapy, ergonomics, and neurorehabilita-
tion. Normative EMG databases play a crucial and significant role in the efficient diagnosis and treatment of neuromuscular 
disorders. They can rapidly provide information that, although not necessarily diagnostic, can efficiently and effectively guide 
further diagnostic studies. Quantitative electromyography (QEMG) in the upper extremities is an effective diagnostic tool, but there 
are currently few normative databases available. The absence of fundamental guidelines and established methods for creating 
normative databases contributes to a significant obstacle in the field of rehabilitation research. This study aims to bridge this 
gap by designing a dynamic, scalable, consistent, available, and partition-tolerant NoSQL database (DB), in alignment with the 
Consistency, Availability, and Partition Tolerance (CAP) theorem, to house normative surface electromyography (sEMG) values 
for upper body muscles, primarily for biometric comparison in rehabilitation. The DB encompasses diverse EMG features, both 
in the time and frequency domains, as well as anthropometric variables, extracted by healthy participants and post-stroke or 
spinal cord injury patients. The participant selection is based on Greece’s average demographic statistics and specific inclusion 
and exclusion criteria from existing clinical trials. The proposed DB is particularly designed to be continuously updated offering 
real-time insights, allowing the DB to be an even more valuable resource for researchers and practitioners working in the field. 

Keywords—Quantitative electromyography, Rehabilitation, Biomedical database, NoSQL. 

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.

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

INTRODUCTION

The electrochemical and mechanical activities that occur 
during the biological events of the human body frequently 
generate measurable signals, known as biosignals, that 
can be analyzed. These signals offer valuable insights into 
not only the intrinsic physiological and pathophysiological 
states of the body, but also into an individual’s affective, 
attentional, and other cognitive states. Such information 
is fundamental in understanding the underlying mecha-
nisms of specific biological systems or events and holds 
significant potential for medical diagnosis.1 

EMG is a specific biosignal generated from electrophysi-
ological changes in muscle fiber membrane conductivity 
and quantifies the electrical currents produced during 
muscle contractions.2,3 EMG sensors can generate informa-
tion relevant to muscular activity and are widely used in 
clinical settings for diagnosing conditions and diseases of 
the central and peripheral nervous systems that involve 
the sensorimotor and somatosensory pathways. 

Quantitative Electromyography (QEMG), offers a valu-
able approach to diagnostics, shedding light on neural 
and muscular disorders. However, a solemn omission 
in the current landscape is the lack of a comprehensive 
normative database (DB) that can serve as a standard for 
biometric comparison.4 This deficiency has far-reaching 
implications, particularly in rehabilitation research, where 
effective diagnosis and treatment are contingent upon 
comparative analyses. 

Surface Electromyography (sEMG) offers a non-invasive 
yet robust way to acquire useful information about the 
human body’s physiological and pathophysiological states. 
Given its non-invasive nature, sEMG has been considered 
an invaluable tool for studying a myriad of conditions, 
ranging from genetic neuromuscular disorders to spinal 
cord injury.5 In recent years, advancements in sEMG 
technologies have positioned them as a complement or 
even a potential alternative to needle Electromyography 
(nEMG) and Nerve Conduction Studies (NCS). However, 
this technological leap is undermined by several limita-
tions.6 This study aims to tackle the lack of standardized 

DBs for sEMG data, which restricts their comparability 
and, as a result, hampers their clinical utility.4,7–9 

The current study aims to fill this void, by creating a 
NoSQL DB with normative sEMG values of the upper body. 
The CAP theorem has been more known in recent years 
as a crucial framework for understanding the limitations 
and potential trade-offs in developing distributed DBs.10 
The theorem states that only two of the following three 
properties—consistency, which ensures that all data 
replicas are synchronized, high availability, which ensures 
uninterrupted access to data for updates, and partition 
tolerance, which allows for continued operation in the 
face of network failures—can be maintained by such a 
system at an optimal level.11 Leveraging the capabilities of 
MongoDB, the DB is designed to be dynamic, scalable, and 
in alignment with the CAP theorem, ensuring Consistency, 
and Partition-Tolerance. Moreover, it aims to incorporate 
a wide spectrum of EMG features, both in the time and 
frequency domains. These features are extracted from a 
diverse participant pool that includes healthy individu-
als as well as those with spinal cord injuries and stroke. 
Additionally, the DB integrates anthropometric variables 
such as gender, age, and Body Mass Index (BMI), with 
the participant selection based on Greece’s average de-
mographic statistics and specific inclusion and exclusion 
criteria from existing clinical trials from the NeuroSuitUp 
and Heroes projects.12,13 The DB not only offers normative 
sEMG values but also hosts raw sEMG signals, enhancing 
its utility for researchers and practitioners alike. 

METHODS AND MATERIALS

Database Design & Schema 

The staggering volume of data generated and processed 
every day is a defining feature of the modern era. Particu-
larly in biomedical research, where data serve as both 
input and feedback for useful insights, this “data deluge” 
poses a unique mix of obstacles and opportunities. The 
management of such enormous datasets requires DBs 
that are not only robust but also flexible enough to deal 
with the peculiarities of the data they are designed to 

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 98

handle. To handle divergent storage challenges, different 
DBs must be designed depending on the situation.14,15 

Traditional SQL DBs are well-suited for handling 
structured data and offer robust query capabilities. These 
DBs have excelled in storage efficiency and data retrieval 
speeds when compared to rudimentary flat file systems. 
However, they often fall short when tasked with handling 
non-uniform or unstructured data, a characteristic that is 
increasingly prevalent in today’s data-rich environment. 
NoSQL DBs have emerged as the tool of choice for managing 
these extensive, heterogeneous, and ever-evolving data 
sets, leading to the rise of NoSQL DBs, particularly those 
of a document-oriented nature.14–16 Studies indicate that 
NoSQL architectures outperform their SQL counterparts 
in almost all performance metrics, including the speed of 
data storage, indexing, and query retrieval.16 

In this study, MongoDB—a document-based NoSQL 
DB—is selected as it is well-equipped to manage such 
inconsistencies. MongoDB resides on the CP side of the 
CAP theorem (Figure 1), meaning it prioritizes consistency 
and partition-tolerance over availability. 

FIGURE 1. MongoDB—CAP theorem.

Though NoSQL DBs like MongoDB are typically 
schema-less, this study utilizes a conceptual schema to 
clarify its architectural structure. The schema (Figure 
2) consists of multiple tables and each table possesses 
unique primary keys and defined attributes suitable for 

storing a variety of data types. The architecture allows 
for straightforward referencing between tables. This in-
terconnection enhances the DB’s robustness, making it 
adaptable to different query requirements and enhances 
its scalability to manage dynamic and multi-dimensional 
data. Moreover, the incorporation of JSON-like documents 
with dynamic schemas not only simplifies data integra-
tion but also offers greater flexibility, thus exemplifying 
modern DB requirements. 

FIGURE 2. Database schema. 

Sample Size 

In this study, stratified sampling is employed. The 
population is going to be divided into homogeneous sub-
populations, or strata, based on gender.17 Analyses have 
suggested that a minimum of 50 subjects per stratum is 
needed to ensure clinically useful confidence intervals; 
therefore, a sample size of 100 participants is selected, 
with 50 participants in each gender-based cell. Previous 
studies have shown that the inclusion of fewer than 50 
subjects per stratum results in confidence intervals that 
lack clinical utility, while studies with more than 75 sub-
jects per group do not significantly refine these intervals.18 

The focus on gender as a stratifying variable was predi-
cated on previous research indicating significant gender 
differences in patterns of muscle fatigue and neuromuscular 
activation during isometric contractions.17 This supported 
the inclusion of gender as a critical stratifying variable to 
gain insights into muscle fatigability and endurance capac-
ity, which are influenced differently in men and women, 
in the pursuit of developing a DB with normative data.17 

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

It is worth mentioning that the selected sample size 
takes also into consideration the influence of data skew-
ness on measures of central tendency and variance. For 
normally distributed data, a stable measure can be ob-
tained with a sample size of approximately 70; however, 
the requirement may vary between 30 and 80 depending 
on the skewness of the data. Provision of skewness as a 
descriptive statistic is highly recommended for future 
studies, in order to enable clinicians to make more in-
formed decisions.19 

Database Validation 

A multi-step approach is used to validate the DB, en-
suring its robustness and clinical applicability. of the data 
stored. Initially, data are acquired from the main cohort of 
healthy participants. Following this, sEMG processing and 
feature extraction techniques are applied to the collected 
data, leading to time and frequency domain analyses. 
Subsequently, statistical metrics such as Means, Standard 
Deviations, and Skewness are calculated. Transforms 
are applied to these metrics to approximate Gaussian 
distributions if the initial data deviate from a Gaussian 
pattern. Z-scores for each subject are then computed. 
Leave-one-out Gaussian Validation is executed to ensure 
optimum sensitivity in the Gaussian cross-validation. 
This method is chosen for its efficacy, despite being less 
rigorous than completely independent cross-validation, 
which is often more resource-intensive.20  Clinical corre-
lations and validity are conducted with a second cohort 
comprising patients with spinal cord injuries (SCI) or 
stroke conditions, evaluated by experienced clinicians. 
Parametric and Non-parametric statistical methods are 
applied throughout the validation process (Figure 3). The 
feedback mechanisms between Gaussian cross-validation 
and statistical metrics, as well as between clinical valida-
tion and sEMG processing and feature extraction, are used 
to fine-tune the DB’s performance and relevance, closely 
aligning it with clinical requirements.21 

FIGURE 3. Database validation. 

FUTURE PERSPECTIVES 

Workflow

An integrated methodology is suggested to strengthen 
the reliability and clinical relevance of our normative 
EMG DB in advance of future research trajectories. The 
workflow starts with a per-person profile that consid-
ers anthropometric data such as laterality, gender, and 
age. The sEMG MyoWare 2.0 Muscle Sensor (Advancer 
Technologies, LLC, version 2.0, Raleigh, NC, USA) is used 
for sEMG Signal Acquisition to record RAW sEMG data, 
which is then stored in the DB. A two-tiered computational 
analysis follows; for the main cohort, an EMG process-
ing and feature extraction process is executed, and the 
resulting data are then stored in the DB. It’s crucial to 
note that a validation cohort is employed for clinical cor-
relations, as validated by experienced clinicians. These 
correlations serve to ascertain the relevance of the DB in 
practical, clinical settings. The validation process for the 
DB, as outlined in the previous section, follows as the next 
step of the workflow adding an extra layer of credibility. 
This workflow, illustrated in Figure 4, seeks to provide a 
normative sEMG DB. 

FIGURE 4. Study workflow.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 100

DISCUSSION 

The current study addressed the lack of availability 
of normative DBs for QEMG by designing a NoSQL DB 
for normative sEMG values of upper body muscles. The 
absence of such DBs poses a significant obstacle in fields 
such as physiotherapy and neurorehabilitation, thereby 
hindering precise diagnostics and effective treatment 
strategies. In alignment with the CAP theorem, MongoDB 
is chosen for its ability to manage large and heterogeneous 
data and a conceptual schema is implemented to clarify 
the architectural structure and guide the data storage and 
retrieval processes. The DB’s applicability is enhanced 
by a stratified sampling method focused on gender. A 
multi-step validation approach is undertaken to ensure 
the DB’s clinical applicability. 

However, the study has its limitations, primarily the 
focus on upper body muscles and the restricted sample 
size. Although the DB is designed to handle a diverse 
range of sEMG data, it has the potential to be more com-
prehensive. Furthermore, stratification can be further 
expanded, contingent upon an increase in sample size, 
offering opportunities for future refinement and increasing 
the level of representation. It is disconcertingly revealed 
through the study that a contemporary, universal meth-
odology for generating a reliable normative EMG DB is 
lacking, and that frameworks for QEMG Normative DBs 
are similarly deficient. 

CONCLUSION 

This study addresses the notable absence of norma-
tive DBs neurorehabilitation research. Capitalizing on 
the flexibility and scalability of MongoDB, a NoSQL DB is 
developed, which integrates sEMG data and anthropometric 
variables from a demographically representative partici-
pant pool in Greece, including both healthy participants 
and post stroke or spinal cord injury patients. The DB is 
designed to be scalable, enhancing its long-term utility 
for clinical diagnostics and rehabilitation research. Vali-
dation protocols and clinical correlations further refine 
its practical relevance. The proposed DB can stand as an 
asset for researchers and is expected to be applied to 
clinical practice in future studies.

ACKNOWLEDGEMENT 

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 con-
text of the project “HEROES: Human Extremity Robotic 
Rehabilitation and Outcome Enhancement for Stroke” 
funded by H.F.R.I.

CONFLICTS OF INTEREST

The authors declare they have no competing interests

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	Digital Transformation Management in Health Services: Health Professionals Perceptions as an Implementation Factor
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	Human Muscle State Machine Using Electromyography Classification with Machine Learning 
	George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2, Panagiotis D. Bamidis1 

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	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*

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