HUMAN BODY AS A MECHATRONIC SYSTEM

Complex Modelling, Simulation and Control

Dorin Andreescu

, Hartmut Riehle

, Florin Ionescu

3,4

and Stefan Arghir

urttembergischer Leichtathletik-Verband e.V., Stuttgart, Germany

University of Konstanz, Konstanz, D-78467, Germany

University of Applied Sciences HTWG Konstanz, D-78462, Konstanz, Germany

Steinbeis Transfer Institute Dynamic Systems, D-10247, Berlin, Germany

University ”Politehnica” of Bucharest, RO-060042, Bucharest, Romania

Keywords:

Human body, Multibody mechanics, 3D CAD, 3D CAE, Identiﬁcation, Modelling, Simulation.

Abstract:

The modelling of the human locomotor system and its simulation are subjects of intensive studies, due mostly

to the development of the computer processing power and the appropriate software. Most ﬁelds of application

are located in the reconstruction of human movements: the motor, its transmission and the necessary control

systems. The goal to have a general approach fully describing the Human Body as a System, with high

accuracy and open set of functions for its entire complexity is still to be reached. Envisioning the body as

an open system we refer to: the 3D-geometry of all bones and their 3D-deﬁned positions (six degrees of

freedom, the corresponding coordinates, restrictions and anomalies), the joints, ligaments and muscles with

their frictions, both viscous and dry, and the contact pressures, the sensorial system with its information

conduits and control mechanism and, last but not least, the human brain, as a hard-soft-controller for this

most intricate and complex system that is the human body. This paper presents some main ideas for this

approach and achieved results concerning steps on the way towards obtaining this goal. The ﬁrst beneﬁciaries

to welcome these results are, on the one hand, the sportsmen and, on the other, the architects and engineers

working on humanoid robots.

1 INTRODUCTION

The goal of this paper is to demonstrate a practical

approach towards 3D modelling and simulation of a

realistic human body. More speciﬁcally, it focuses, as

a ﬁrst step, on how to gain the most valuable informa-

tion about a real athlete.

Practically, the paper is focusing on the modelling

of the body and the locomotor system as an inverse

dynamic paradigm. While any human mechanical

system can be moved with direct and inverse dynam-

ics, we have selected the second approach, as conve-

nient for research stage (Andreescu et al., 2007). Thus

one can consider, that any human body can be de-

scribed as a speciﬁcally adapted abstraction as a gen-

erally deﬁned solid multibody system. It follows the

CAD-methodology typical for machine or plant de-

sign. This approach will be illustrated, step by step,

in the following chapters and accompanied by appro-

priate ﬁgures.

The body receives information through various

sensors distributed all over the body. This includes:

hearing (both frequency and amplitude are qualita-

tively ”understood”), seeing (concerning images as

forms, colours, distances, movements) tasting, touch-

ing (concerning pressure, temperature and humidity),

smell. Information from outside the body is received

by the different sensorial organs via different types

of energy corresponding to each of the ﬁve human

senses. Data from the sensors are then directed to

the brain directly or indirectly through various relay

stations in the form of bio current.

In Figure 1 the sensors are noted with s

, while

denotes, in a simpliﬁed manner, some well known

hand and foot joints. The brain receives and processes

all the signals in order to produce an effect on the en-

vironment.

To obtain a Model of the Human Body to be sim-

ulated, two paradigms of thinking are used, by adopt-

ing the manner of robot modelling (Stefanoiu et al.,

2004; Borangiu and Ionescu, 2002):

339

Andreescu D., Riehle H., Ionescu F. and Arghir S..

HUMAN BODY AS A MECHATRONIC SYSTEM - Complex Modelling, Simulation and Control.

DOI: 10.5220/0003600903390342

In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2011), pages

339-342

ISBN: 978-989-8425-78-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: The human body as an open system.

1. The Direct Dynamics (DD), which corresponds to

the previously described mode: starting from the

brain a set of commands are provided via nerves

to the muscles giving them the order to act; the

body is moving according to the set of commands,

by consuming energy. During the movement the

brain is continuously correcting the command set,

in order to attain the desired input variables set;

2. The Inverse Dynamics (ID) implies a different

way for the ﬂow of information and it only exists

in theory: the realistically achievable movement

of different reference points (joints, for example)

is given, from what the forces and moments of all

joints. Thus, all the human body parts are moved

as a result of the energies calculated to be needed

to be introduced into the body via the imposed

movements.

The ID paradigm was used for the present re-

search. The model to be simulated is a reconstruc-

tion/replica of a real skeleton. Having chosen a spe-

ciﬁc skeleton, a real human being must be found hav-

ing very close constitutional resemblance. On this

real human being it can measured, by using appro-

priate techniques, the movement of the appropriately

selected joints/points. Afterwards, data have to be

denoised and organised as ASCII ﬁles. Through the

ASCII ﬁle, these points can be assigned to the corre-

sponding points on the Computer Model.

2 MATHEMATICAL

FORMULATION

These interactions can be formulated as differential

equations of different types: mechanical, hydraulic,

electrochemical, and other. In the general form, such

a mechanical equation can be depicted as in 1. With

several such equations, a system is formed. Reference

for mathematical modelling of the human body can be

found in (Hanavan, 1964; Ballreich, 1996)

[M] · { ¨q

} + [D] · { ˙q

} + [C] · {q

} =

[A] · {

} + [B] · {Q

} + [E] · {U

} (1)

with: i ∈ [1, ..., 6n], k ∈ [6n − 5, ..., 6n], [M]-inertia

matrix {kg or Nms

−2

}; n - total number of de-

grees of freedom (DOF), [D]- the Damping Matrix

{in N/(m/s) or Nm/(rad/s)}; [C] - elasticity matrix

{N/m or Nm/rad}, [A] -coefﬁcients’ matrix of the

ﬁrst derivative of the perturbation vector {Q}; [B]-

coefﬁcients’ matrix of the perturbation vector; [E]-

coefﬁcients’ matrix of the input vector {U}.

3 FROM HUMAN SUBJECT TO

MODEL SKELETON

In order to model an simulate the human locomo-

tor system the authors applied a two step approach

(Riehle, 1979; Vieten, 2004; Zahran et al., 2002).

First the bones need to be mechanically modelled. For

this purpose a human skeleton was used. Each bone

is modelled independently.

After having the bones as separate static models,

they can be combined with joints to form a piecewise

modelled skeleton. Nevertheless, this is still a static

model. At this point we have obtained just an innert

object with no laws to move it or restrictions for the

movement.

The animation of the model will be done by mim-

icking the movements of a real human body. For this

purpose, a human test subject is needed. This per-

son must be healthy, at least without any locomotor

problems or peculiarities and he or she must have ap-

proximately the same dimensions as the skeleton used

previously to obtain the static model. More details on

the actual modelling are provided in Chapter 5.

4 DIRECT AND INVERSE

DYNAMICS

The direct dynamic problem consists of ﬁnding a

transformation matrix that relates the reference coor-

dinate frame to the reference frame of each segment

in a kinematic chain. In other words, the direct dy-

namic model determines the actual motion of a body

when certain forces and or moments of force are ap-

plied to certain points. Typically, it uses link-segment

models to represent the mechanical behaviour of in-

terconnected segments, such as the limbs of humans,

SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and

Applications

340

animals or robots. Thus, the movement is completely

deﬁned by the aggregated movements of all degrees

of freedom. Presuming that each articulation has a

maximum of 6 degrees of freedom, then the set of

equations described in 2 completely describes the cin-

ematic chain.

{ ¨q

} = −[D] · [M]

−1

· { ˙q

} − [C] · [M]

−1

· {q

+[A] · [M]

−1

· {

} + [B] · [M]

−1

· {Q

+[E] · [M]

−1

· {U

}

(2)

Inverse rigid-body dynamics is a method for comput-

ing forces and/or moments of force (torques) based

on the kinematics of a body and its inertial properties

(mass and moment of inertia). Opposite to the direct

dynamics, given the kinematics of the various parts,

the inverse dynamics derives the minimum forces and

moments responsible for the individual movements.

In practice, inverse dynamics computes these internal

moments and forces from measurements of the mo-

tion of limbs and external forces such as ground reac-

tion forces, under a special set of assumptions.

A general procedure is given in Figure 2.

The ﬁrst step was to identify a suitable structure.

All bones of the human locomotor system were

identiﬁed. Each bone was described in terms of

weight, density and dimensions. All bones are con-

sidered to be perfectly rigid.

All joints were determined. The normal orienta-

tion for the coordinate frame was established for each

joint. The second step was to identify measurement

points on the skeleton model as shown symbolically

in Figure 3a

5 MODELLING

The modelling and simulation environment of choice

was Solid Dynamics SDS (SolidDynamics, 2005).

This software can only ”animate” solid bodies by ap-

plying predeﬁned forces/torques. Paradigmatically,

this corresponds to the direct dynamics mode. As a

result, our case is basically an inverse dynamics prob-

lem: from the recorded movement of a real person,

the necessary forces are to be determined in order to

move each joint. This is shown in Figure 3b.

After selecting the segments of interest, several

markers were placed on the on the body of the ath-

lete.

Recording point movement was accomplished us-

ing procedure described in Figure 3b.

Modelling was done using mirrors in order to gain

more visibility on the markers. In case that during a

movement a certain marker gets hidden from a certain

Figure 2: Solving the inverse dynamic problem.

Figure 3: a) Identifying markers between real body and

model; b) Solving the inverse dynamic problem.

camera, then maybe that marker can be viewed in-

directly in one of the mirrors, thus insuring constant

marker tracking. Afterwards, during image process-

ing, virtual images in the mirror must be transformed

relative to the basis coordinate frame.

On the ASCI ﬁle, a FFT low pass ﬁlter was applied

in order to remove all the noise from the data. From

this processing, the path of each marker relative to

time is determined.

At this point, in order to calculate the forces driv-

ing the bodyes, all relevant masses are needed. To

this end, and considering a mean bone density, only

the volume of the bones must be determined. The

volume for each bone can be easily extracted from its

3D shape.

A 3D model of the bones is obtained by following

the procedure described in Figure 4.

Figure 4: Obtaining a 3D model of a bone.

HUMAN BODY AS A MECHATRONIC SYSTEM - Complex Modelling, Simulation and Control

341

Incidently, the scanning process provides noisy

outputs. Nevertheless, after some ﬁnal manual modi-

ﬁcations, each bone from the locomotor system has a

3D model.

By having a 3D model of all the objects, based on

their dimensions and an average bone density, a mass

can be attributed to each one.

As a result, the force applied to each marker can

be easily determined from the formula F(t) = m·a(t).

6 SIMULATION

By applying this method, various techniques from dif-

ferent sports and other physical activities have been

modelled. Two examples are shown in Figures 5 and

Figure 5: Athlete sprinting. 3 consecutive frames.

Figure 6: Piano playing. 3 consecutive frames.

7 CONCLUSIONS

The aim of the work was to illustrate a methodology

of representing the human body as a mechatronic sys-

tem. The modelling was performed in the multibody

technology and the preferred tool was the SolidDy-

namics Modelling and Simulation program.

Obtaining the models and animating them exceeds

the normal frame offered by SDS and its environment.

Thus, other means had to be employed.

An own technology was developed and presented

for obtaining models of the human body via scanning

of bones and their translation into generalised meshes

to be afterwards endowed with structural and dynam-

ical properties.

Different layers of pre processing software were

developed and are brieﬂy presented in the paper. The

paper demonstrates that the implementation of mod-

ern technologies allows the modelling and simulation

of the human body as a most complex bio mechan-

ical system. New approaches and new generations

of computers will facilitate more and more complex

approaches, as already mentioned, as well as shorter

computing times, (quasi) real time simulations, the

gradual implementation of FEM components and a

bio control strategies for both direct and inverse dy-

namics.

Possible applications include medical care, post

traumatic or post accidental recovering, rehabilitation

of handicapped persons. In conjunction with the CAE

of medical and ﬁtness machines as well as for sport ar-

ticles as shoes,and others, important information can

be obtained for an optimised design of these products

through simulation. The achieved results and the aux-

iliary methods used are quite promising. This opens

new prospects for further developments in various in-

dustrial applications.

ACKNOWLEDGEMENTS

The authors would like to express their gratitude for

the support to the University of Konstanz/Institute

of Sport Sciences, Deutsche Forschung Gemeinschaft

(DFG), Steinbeis Transfer Institute Dynamic Systems

of the Steinbeis University Berlin, and HTWG Kon-

stanz Germany.

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