Towards the Virtual Human Simulator
Giovanni Saggio
Department of Electronic Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, Rome, Italy
Keywords: Virtual Human Simulator, Digital Twin, Human Digital Twin, Digital Angel.
Abstract: Up-to-date technologies make it possible to acquire a considerable amount of data, for a considerable period
of time. It follows the possibility to strategically count on the necessary and useful elements to characterize
from simple to very complex systems. Acquisition, even prolonged, and characterization of data from a certain
source (object, component, system, etc.) make it possible to create a virtual copy of this source, namely Digital
Twin (DT). When technology of DT meets smart algorithms of machine learning and artificial intelligence,
the Intelligent DT (IDT) arises. DTs and IDTs are successfully adopted in electronics, mechanics, chemistry,
but can they be applied for the entire human body so to realize a virtual human simulator (VHS)?.
1 INTRODUCTION
Sensors, transducers and related electronics allow
gathering huge amounts of different types of data
from any particular system during long time periods.
In principle, this means to count on all the necessary
elements to fully describe any physical system, such
as an object, a component, a mechanism, a network,
an implant, a machinery, a structure, potentially of
any level of complexity, whether inanimate,
vegetative, and animate too.
A complete description gathered from data
potentially allows making a full virtual copy of any
system in a digital format, namely a Digital Twin
(DT). An uncomplete description allows making a
partial virtual copy, namely a Model, which
corresponds to a sort of limited version of the DT.
Electronic technicians virtually assemble models
or DTs of electronic components to simulate their
interactions in a network before making its real
counterpart, since the very early 1950’s when an
electromechanical-relays based computer was
successfully adopted for simulating the sinusoidal
steady state of a linear network (Graham, 1953).
Starting from that first experience, new fields of
computer-aided circuit analysis and computer-aided
design science were born.
Apart specific cases, an evolution occurred to land
nowadays from limited concept of Model to the
broader concept of DT.
A key step was the introduction of the conceptual
idea of Product Lifecycle Management (PLM), by M.
Grieves (Florida Institute of Technology) on 2002
(Grieves, 2005), as an efficiency-promoting
paradigm for complex, manufactured products. In
particular, within the concept of PLM is a real space
that furnish information to a virtual space where data
are elaborated to give useful knowledge to run
processes in the real world. The virtual space is also
made of subspaces to partialize possible simulations,
such as modelling, testing, optimization, etc. (Figure
1). Within this frame, “lifecycle” emphasizes the
dynamicity of the process through the phases of
creation, production, operation, and disposal.
Figure 1: Conceptualization of PLM as a mirroring or
twinning between a real physical system and its virtual
counterpart. Data flows are from the real to the virtual
space, information process flows are, vice-versa, from the
virtual to the real space, the virtual space is arranged in
virtual subspaces 1, 2, .. n.
Saggio, G.
Towards the Virtual Human Simulator.
DOI: 10.5220/0011948100003414
In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 5-12
ISBN: 978-989-758-631-6; ISSN: 2184-4305
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
5

2 DIGITAL TWIN
The concept of PLM for industry was referred as
Mirrored Spaces Model (MSM) for a university
course, and as Information Mirroring Model (IMM)
for a seminal book (Grieves, 2006), and later in a
more descriptive way as Digital Twin (DT) as “a set
of virtual information constructs that fully describes
a potential or actual physical manufactured product
from the micro atomic level to the macro geometrical
level” in a paper dated 2011 (Grieves, 2011).
To further complicate matters, the terms Digital
Angel (DA), and Virtual Entity (VE) were introduced
too.
DT has becoming more and more crucial for
realizing the “Industry 4.0” paradigms, as already
implemented by several companies, such as Siemens,
Toyota, DHL (Dohrmann et al., 2022), Philips (van
Houten, 2018), IBM (IBM website, 2022), General
Electric (GE website, 2022), Oracle Corporation
(Puri, 2017), Microsoft, to cite a few. The interest on
DT is witnessed by the increasing google search
(Figure 2a,b), and the number of published papers
(Figure 3) during the latest years.
a)
b)
Figure 2: a) Growing interest in google search od “Digital
Twin” since 2016, b) as differenced in Countries, the China
being the most active (grey colour is related to few searches,
light blue and blue the Countries with major numbers of
searches).
Figure 3: Number of papers including the words “Digital
Twin” from 2016 (263) to 2022 (26,200).
The effectiveness of the DT strictly depends on
how faithful is the virtual entity with its real
counterpart. Virtual vs. real correspondence depends
on data gathered from the real world (Figure 1), in
particular their number, accuracies, levels of
significance and abstraction (Jones et al., 2020).
Moreover, the correspondence is based on the
algorithms that, depending on data, describe the
physical system, and on processes that foreseen
actions to be done to the real part (Kuhn, 2017; Rosen
et al., 2015; Boschert et Rosen, 2016).
DT, as a complete virtual description of a physical
product that is accurate to both micro and macro
level, splits into:
 Digital Twin Prototype (DTP): the virtual
description of a prototype product, containing
all the information required to create the
physical twin
 Digital Twin Instance (DTI): specific instance
of a physical product that remains linked to an
individual product throughout that products life
 Digital Twin Aggregate (DTA): the
combination of all the DTI
 Digital Twin Environment (DTE): a multiple
domain physics application space for operating
on DTs, including performance prediction, and
information interrogation
When the DT meets artificial intelligence,
machine learning and deep learning algorithms
(Costantini et al., 2022), the paradigm of Intelligent
Digital Twins (IDTs) arises (Grieves, 2022; Sahlab et
al., 2021). The way is from simulation (as
counterfeiting) of reality (a simplest copy of the real)
to replication of reality.
The introduction of the DT and the increased time
devoted to it with respect to its real counterpart has
been leading to a number of advantages:
 The reduction of tests and checks (and,
consequently, time and cost savings)
 Time-to-market reduction
BIOSTEC 2023 - 16th International Joint Conference on Biomedical Engineering Systems and Technologies
6

 Lower any product defect
 Intercept potential problems before happen
 Simulating events without causing them in the
real system
 Evaluate aging effects
 Evaluate all parameters at once avoiding
considering them one-by-one
 Limit the production as a final step only
To evidence those advantages, we can consider
the potential stream of a generic physical system,
which can evolve into predicted or unpredicted
behaviours, which can in turn evolve into desirable or
undesirable (Figure 4).
Figure 4: Classifications of all possible evolutions of a
physical system (Grieves & Vickers, 2017).
Predicted Desirable (PD) is the designed class;
Predicted Undesirable (PU) is related to unsolved
problems (to be taken into account to avoid lawsuits);
Unpredicted Desirable (UD) does not result in
problems but is an index of incompleteness or the
adopted model; Unpredicted Undesirable (UU) holds
potential serious and dangerous problems that must
be mitigated or solved. The latter is the class for
which DT can be strategical advantageous.
According to the aforementioned advantages, the
concept of DT has been successfully adopted in many
different areas, such as aerospace (Caruso et al., 2020;
Piascik et al., 2020), automotive, manufacturing (Lu
et al., 2020), production (Wagner et al., 2019),
industrial and consumer packaged goods, food
process (Verboven, 2020), pharmaceuticals
industries, energy management (Agouzoul et al.,
2021), maintenance optimization (Xia & Zou, 2023),
distribution grid (Jiang et al., 2022), and more.
3 VIRTUAL HUMAN
SIMULATOR
Among all, the most disruptive area of application of
the concept, of whatever DT or IDT, is in the medical
one, for which an evolution is towards the Human
Digital Twin (HDT), also named as Virtual Human
Simulator (VHS).
VHS is not just a simple evolution of DT. The
latter refers to a physical system, VHS refers to a
behaviour, biological and physical system in an
ensemble.
3.1 Steps
The higher the correspondence between the real and
the virtual, the higher the number of possibilities the
VHS allows, as described in the following points
mentioned with increased complexity level:
1. Digitize: analogue to digital data
conversion
2. Visualize: digital representation of a
physical system
3. Simulate: determine one or more
behaviours of the physical system in its environment
4. Emulate: mirror a system by imitation
5. Extract: gather information from real data
streaming
6. Orchestrate: virtual control or update of
physical system
7. Predict: future behaviour of the physical
system
The digitizing step can be considered as the results
of measurements made based on a four type
schematization, according to the positions of
electronic sources and sensors with respect to the
human body under measure (Saggio & Sbernini,
2011):
 Outside-In (sources on the body and sensors
elsewhere, e.g. optical systems (Saggio et al.,
2020));
 Inside-Out (sensors on the body and sources
elsewhere, e.g. accelerometers (Ricci et al.,
2019; Saggio et al., 2021))
 Inside-In (sources and sensors on the body, e.g.
sensory gloves (Saggio et al., 2011) or electro-
goniometers (Saggio et al., 2014))
 Outside-Out (sources and sensors not directly
on the body, e.g. force plates (Costantini et al.,
2018)).
Data gathering have been evolving from fixed
sensors (Inside-In), to mobile sensors (Outside-In,
Inside-Out) to immersive sensors (Outside-Out,
immersive persistent ambient, ubiquitous data).
The visualizing step can be realized by means of
a monitor or an hologram (Ferrari et al., 2012; Saggio
& Ferrari, 2012), showing virtual (Saggio et al.,
2009b) or augmented reality, going from pixels (2D
on screen) to voxels (3D on metaverso), or by 3D
System behavior
Predicted
behavior
Unpredicted
behavior
Predicted
undesiderable
(PU)
Predicted
desiderable
(PD)
Unpredicted
undesiderable
(UU)
Unpredicted
desiderable
(UD)
Towards the Virtual Human Simulator
7

printed structures such as human organoids
(Steimberg et al., 2020; López-Tobón et al., 2019).
The simulation step can be realized by means of
multi-physics software modelling mechanical,
electrical, biochemical cues useful for designing
proof-of-concept models of human systems and
subsystems (Zheng et al., 2021).
The emulation step can provide test beds for a
broad range of human behaviours experimentally
(e.g. the neon project by Samsung; Caliani, 2020),
useful for better understanding the human
biomechanics, biophysics, biochemistry and
energetics (e.g. of the ankle-foot mechanism (Au et
al., 2006) or sign language implementation (Calado et
al., 2021)).
The extraction step can allow determining
differences between ideal vs. non-ideal behaviours
(e.g. healthy vs. pathological human walking
assessments (Verrelli et al., 2021)).
The orchestrating step can take advantages from
the extraction step to modify the incorrect behaviour
of the physical system toward the desired one (e.g.
specific vitamins requirements across the human life
cycle (Youness et al., 2022)).
The prediction step can provide future effects of
current behaviours (e.g. dietary habits; Lentz,
(2008)).
3.2 Behavioural Model
The behavioural pattern of an individual depends on
a complex mechanism due to the contribution of
environmental characteristics (climate, pollution,
etc.), his/her physical conditions and evolutions (sex,
age, deficiencies, impairments, etc.), psychological
status and developments (family situations, working
conditions, friendships, etc.), boundary conditions
(holidays/weekdays, day/night, sounds/noises, etc.),
and more.
Therefore, as a matter of principle, if related data
are fully available a complex VHS can be potentially
realized, for which every environmental
characteristic, every physical development, every
psychological development, and every boundary
condition can be simulated, each as one of the n
interacting subspaces (Figure 1). As an example, we
can think to a VHS identity to determine, describe and
predict a child’s character (Mohammadi et al., 2018).
3.3 Physical Model
The physical model of an individual depends on a
complex mechanism due to the contribution of
historical data (past muscular activities, lifestyle, type
of work, etc.), metabolism, physical characteristics
(Saggio & Costantini, 2020) (individual muscular
forces, contact forces and joints, elastic energy in
tendons, antagonistic muscle action, etc.).
Therefore, as a matter of principle, being able to
count on related physical data, it is possible to create
a complex VHS so to be able predicting the future
muscular behaviour of an individual, as already
introduced for athletes’ activity (Barricelli et al.,
2020).
3.4 Biological Model
The functioning of an individual’s body depends on a
complex mechanism due to the contribution of
systems (circulatory, muscular, endocrine, nervous,
immune, etc.), subsystems (respiratory, vestibular,
endocrine, locomotor, digestive, etc.), organs (heart,
liver, spleen, lungs, etc.), influenced by surrounding
conditions (temperature, vasodilatation, hydration,
etc.) and regulated by brain activity.
Therefore, as a matter of principle, counting on
complex related data, a complex VHS can be realized.
Each system, apparatus or organ can be seen as
subspaces in the partialization of the possible
simulations (Figure 1), all interacting in a single (on
cloud) platform, as already introduced for cancer
evolution issues (Greaves & Maley, 2012).
3.5 Data
Data for VHS can be categorized into:
 Physical, chemical, electrical
 Current (in real-time, on-line), historical
(deferred time, off-line)
 Transient, permanent
 Primary (necessary, determining the accuracy
degree), secondary (optional, improving
accuracy)
According to previous sections, data for VHS are
not “simply” limited gathered from the humans, they
have to be collected from his/her surroundings too.
Collected data can be advantageously wireless
sent to cloud systems to be stored and processed.
3.6 Development
For the VHS development, a partnership between
Medicine, Biology, Engineering and Computer
Science is fundamental and strategic, so to be able to
monitor, Analyse, Simulate, Visualize, Manage the
VHS, to make it Descriptive, Integrative and
Predictive.
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The creation of behavioural and biological models
is made possible by data classification through
Artificial Intelligence and Adaptive Networks, via
machine learning algorithms. For VHS, the most
appropriate models to start from are given by kernel
neural network (kNN) and support vector machine
(SVM).
A descriptive language for the VHS could be
given by the Unified Modelling Language (UML)
(Saggio et al., 2009a), by a development platform
such as the XMPro (Xmpro.com, 2022), and a
specific software such as the ScaleOut
(Scaleoutsoftware, 2022).
3.7 Advantages
As an example, let’s observe how VHS can be useful
in a crucial sector such as that of the appropriate and
safe use of medicines. It is estimated that around
200,000 people die every year in Europe from the
negative consequences of drug reactions (Aynaci,
2020). This high number of deaths can be drastically
reduced by implementing the VHS, simulating, as
mentioned, the evolution of the parameters of the
specific patient linked to the assumption of the
specific drug.
Obviously, this is only one aspect (not at all
negligible) of using a VHS and its possible
advantages. The VHS, in fact, on the basis of the
interaction of the aforementioned n subspaces, allows
evaluating the possible side effects of a drug before it
is actually administered to the particular patient or
average of patients.
For example, of a drug used in subspace 1 “heart”,
its undesirable effects can be simulated on subspace
2 “liver”, thanks to the analysis of the
combined/separate effect between the two spaces.
Furthermore, the VHS allows switching from the
culture of the administration of drugs/therapies based
mainly on the specific pathology (one-size-fits-all) to
one based on the specificities of the patient (tailor-
made- o personalized- medicine). Moreover, the VHS
allows evaluating drugs and therapies effectiveness
and, consequently, producing lower costs for health
assistance (Scharff, 2010). Finally, thanks to the
possibility of creating scenarios and carrying out
simulated tests in unlimited numbers, the VHS leads
to the reduction (up to zero) of the use of laboratory
animals, while predicting and developing life-saving
events.
3.8 Key Points
Key points to effectively realize the VHS can be listed
taking a cue from the four of the six “M” of K.
Ishikawa, i.e. Machine, Method, Material, and
Management, leading to:
 Design cooperating human body simulation
algorithms (anybody modelling system), with
the possibility of integrating already known
models (of organs or systems)
 Determine the most suitable data collection
network (e.g. wireless body area network)
 Collect the subject's history data (historical
characteristics), but with an incremental system
(incremental learning data) to avoid a collapse
or excessive response time of the system
 Create a cloud system for data collection,
storage and processing (cloud computing)
 Determine the missing necessary data (Batista
& Monard, 2002)
 Select data with high information content
(feature selection) eliminating those with little
(noisy data) or no information content (feature
extraction)
 Determine the appropriate machine-learning
approach and algorithm(s)
 Design a graphical user interface (GUI) easy to
consult even for non-technicians
 Create the role of supervisor(s) who highlights
critical points of the approach based on the
results obtained
 Test the simulation algorithms by entering
known data and detecting the output data (a-
priori known) verifying their correspondence
to the real ones
 Evaluate the correspondences of VHS results
with the real ones, to remodulate the previous
points (Zhang et al., 2022).
4 CONCLUSIONS AND
OUTLOOKS
Full information can be gathered from any real
physical systems to create their virtual counterparts
known as Digital Twins (DTs) (partial information
leads to a Model).
Sensors, transducers and related electronics are
the medium to gather information and to make real
and virtual interacting.
DT has been successfully applied in a variety of
different areas, in some of which it’s importance
become mandatory.
Towards the Virtual Human Simulator
9

In particular, in the medical field a number of
different virtual human organs have been realized,
such as the complex heart (Levy et al., 2019).
Not limiting to one virtual organ, the DT concept
can be, in principle, expanded toward the entire
human being, so to land to the concept of Virtual
Human Simulator (VHS).
By developing the VHS, outlooks can be:
 create connections between two different
VHSs to highlight differences and critical
issues
 remotely monitor the subject (progress/
regression evaluation)
 supply the drug routines for the single subject's
VHS
 facilitate the possibility of moving towards
non-invasive and painless medical tests.
In futuristic perspectives, the VHS technology
will be able to simulate brain waves in response to
generic or specific stimuli, allowing investigations
about the functioning of the human brain aimed at
improving neuro-technologies.
Furthermore, VHS will be able to give rise to
virtual models of bacteria and viruses, allowing to
study the mechanisms of interaction with the various
organs of the human body up to the possible onset of
pathologies, and consequently to evaluate a-priori the
effectiveness of possible pharmacological remedies.
Theoretically, VHS will be possible, let’s see in
practice.
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