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Acta Polytechnica Vol. 50 No. 4/2010

A Study of Gait and Posture with the Use of Cyclograms

O. Hajný, B. Farkašová

Abstract

Present-day science makes extensive use of the simulation. Our work focuses on simulating human gait. Simulations of
human gait can be used for prosthetics and therapy e.g. rehabilitation, optimizing the movements made by sportsmen,
evaluating advances in rehabilitation, etc. Methods of AI can also be used for predicting gait movement and for identifying
disorders.
Our project is about measuring human gait, simulating the musculo-skeletal system in order to study study walking

and predicting and quantifying gait with the use of neural networks. The research is being carried out in the biomechanics
laboratory at FBE CTU, and is intended for use in clinical practice at the 2nd Faculty of Medicine, Charles University.

Keywords: simulation, human body model, walking, artificial intelligence, gait angles, bilateral cyclograms.

1 Methods
For a study of gait angles, we decided to use meth-
ods based on measurements of the geometric proper-
ties ofbilateral cyclograms(also calledangle-angledia-
grams). The symmetrymeasuresare simple andphysi-
callymeaningful, objective, reliable andwell suited for
a statistical study [1]. Furthermore, the technique is
strongly rooted in geometry and the symmetry mea-
sures are intuitively understandable [3]. Depending
on the cyclicity of the gait, cyclograms are closed tra-
jectories generated by simultaneously plotting two (or
more) joint variables. In gait study, the easily iden-
tifiable planar hip-knee cyclograms have traditionally
received the most attention. In order to quantify the
symmetry of human walking, we also obtained and
studied cyclograms from the same joint from two sides
of the body [2].

Fig. 1: Illustration of marker movements

1.1 Measuring systems

We used two methods for measuring gait and move-
ment: an infrared (IR) camera with active markers,
and a web camera. First, we had to measure a human
gait to obtain a quantumofdata. For this purpose, we
used two methods of movement in the space measure.

The firstmethod used an IR camera (Fig. 2.) with ac-
tivemarkers (LukotronicAS 200),whichwas available
fromthe externalworkplaceJointCTUDepartmentof
Biomedical Engineering and Charles University. We
placed LED diodes markers on the measured person
at the following points: malleolus lateralis, epicondy-
lus lateralis, trochanter major and spina iliaca ante-
rior superior. By this method we were able to register
the movement in three dimensional space. The sec-
ond method was to record a video of human walking,
using a web camera. The video was consecutively an-
alyzed in Coach6, version 6.1. In this case, we made
our own circular markers, which contrasted with the
clothing of the measured person, who was dressed in
black. TheCoach6programis anadequate tool for de-
tecting themarkers. We chose frames of the video that
were usable for our analysis, and we marked the posi-
tions of the markers in them, one by one. This video
method providedonly two-dimensional co-ordinates of
the captured markers.

Fig. 2: LUKOtronik AS200 IR camera system [6]

1.2 Model

A model of human body was created in the Matlab
(Mathematic Laboratory), version R2008b environ-
ment with SimMechanics (Simulink toolbox) for sim-
ulating and modeling mechanical elements and their
directions. To create a model of the human body we
used the SimMechanics tool blocks. For a practically

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Acta Polytechnica Vol. 50 No. 4/2010

useable model, a big block was built to form the base
of the model. A skeletal was formed by a body block
and custom joints. With the help of the joint actua-
tor block, the data acquired by one of the measuring
systems was imported to the body model. Using the
joint sensor block, data could be exported from the
body model (Fig. 3).

Fig. 3: Model of a human body created in the Simulink
workspace [5]

The presentmodelwas updated by themass of the
body. The user working with the program filled in
a small form including information on height, weight
and sex. With a subprogram we computed the length
and weight of each segment of subject’s body.
Several methods can be used for determining the

weight of the individual segments. Unfortunately,
none of them is absolutely exact, so we have to count
with a weight error. We decided to calculate the mass
of the segments by means of experimentally acquired
coefficients. The coefficients are stochastic, and apply-
ing them for an average population involves a weight
error, because the authors (Zatsiorsky, Bohn, Shan, et
al.) carried out their experiments on a different pop-
ulation.
We wanted to calculate the weight of the individ-

ual body segments. The optimal equationusing exper-
imentally appointed coefficients B0i, B1i a B2i takes
the form:

mi = B0i + B1i · m + B2i · h. (1)

Where mi is the weight of an individual seg-
ment (kg), m is the total weight of the subject, and h
is the height of the subject (cm).
Table 1 shows our experimentalweight coefficients.

Other methods for finding out the weight of the seg-
ments can also be used, e.g. subaqueous weighing ac-
cording to the Archimedes principle. However, this
method is not applicable for our example.
We can compute the loading of the joints and the

moments of inertia of the body segments using a sim-
ilar equation:

Iti = B0i + B1i · m + B2i · h (2)

Table 1: Weight coefficients for computing the weight of
the segments

Weight coefficient

segment B0i B1i B2i

Foot −0.8290 0.0077 0.0073
Shin −1.5920 0.0362 0.0121
Femur −2.6490 0.1463 0.0137
Hand −0.1165 0.0036 0.0017
Forearm 0.3185 0.0144 −0.0011
Upper arm 0.2500 0.0301 −0.0027
Head 1.2960 0.0170 0.0143

Upper torso 8.2144 0.1862 0.0584

Medial torso 7.1810 0.2234 −0.0663
Lower torso −7.4980 0.0976 0.0490

Our body model was created mainly to calculate
the angles in the joints and to use the results in the
simulation of human gait. To compute the angles we
used equation (3):

cosϕ =
u1v1 + u2v2√

u21 + u
2
2 · v21 + v22

(3)

for the two-dimensional system, where u1, u2, v1, v2
are thevectorsof thebody segments (femur, shin, foot,
etc.) represented by at least two points as markers.

Fig. 4: Figure of the computed angle in the knee

For computing the angles in the three-dimensional
system, the model uses the following equation:

cosϕ =
u1v1 + u2v2 + u3v3√

u21 + u
2
2 + u

2
3 ·
√

v21 + v
2
2 + v

2
3

(4)

Based on this equation, the model is able to calcu-
late the angles in the hip, knee and ankle if there are
enoughmarkers. In the LukotronicAS200 system, the

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Acta Polytechnica Vol. 50 No. 4/2010

maximum number of markers is 32, but we had a lim-
ited editionwith 10markers. With 32markerswewill
be able to measure more joints in the body, including
the upper extremities, but this is beyond the scope of
our project.
By means of simulationwe can check the accuracy

of the data. Becausewe use more than one system for
imaging the movements of a point in space, the mark-
ers may be confused or another human lapsesmay oc-
cur.

Fig. 5: Animation controlled and computed by the
Simulink model [5]

The model uses measured data of a moving point
in space to calculate the angles in the joints. To calcu-
late the angle in one jointweneed at least three points
(markers). This data was acquired using a webcam
and was processed in Coach6, version 6.1. The frame
rate of webcam was 26 frames per second.

2 Results
By means of the model we obtained graphs of the de-
pendency of the change in angle over time in the knee
and hip (Fig. 6). This is important for subsequent
computation of the cyclograms. The graph is plot-
ted fromthe data obtained from thewebcammeasure-
ments. The graph captures the half step and the full
step, because the subject was asked to make a step
from the standing position (Fig. 1). It follows from
the graph that the angle in the knee changes from 1◦

(stretched leg) to 78◦ (relaxed leg), where the values
represent the angle between the femur and the shin.
The second curve plots the changing angle between
the femur and the body.
The cyclograms are obtained very easily. They are

graphs plotting the angles in the knee and the hip to-
gether. There are various kinds of cyclogram where
we can plot e.g. the angles between the knee and the
ankle, etc. (Fig. 6). For our case, we used the formula:

cyclogram=
angle in knee

angle in hip
(5)

Fig. 6: Computed changes in the angles over time of the
knee and hip

We had to decide which kind of cyclogramwas ap-
plicable for our research. The choice fell on the cyclo-
gram of the knee and the hip, because it is easiest and
most accurate to measure the angles in the joints.

Fig. 7: Cyclogram of gait (knee/hip)

3 Gait prediction

We selected an artificial neural network to analyze the
cyclograms, because we consider NNs to be very in-
teresting, and we think they will be used extensively
in future [4]. The M-S (musculo-skeletal) body model
contains an option of movement prediction, or more
accurately inferior limbmotionpredictionwhich is rep-
resented by the NN. We used the Matlab AI toolbox
for the NN.
We created our NN to predict the angles in the

knee and hip of the right leg and we used a backprop-
agation network training functionwith 10 input layers
(we defined this number according to a calculation of
the breaks in the angle functionbehavior)andoneout-
put layer. Weuseda log-sigmoid function as a transfer
function of the input layers and a linear transfer func-
tion in the output layer. Our neural network learns
for 500 generations, because the mean squared error
(MSE) of the predicting function of the angles in the
knee and the hip was the smallest at that point.

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Acta Polytechnica Vol. 50 No. 4/2010

Fig. 8: Data predicted by NN

4 Conclusion
We designed a functional user interface in the Matlab
GUIDEcomponent inwhich theuser can easilyhandle
all parts of our program.
The output of our project is a user interface for

work with our application consisting of an analysis
(graphs of measured data and of the change of an-
gles in the knee and hip, bilateral cyclograms), a body
model (animation ofmotion) and a prediction (setting
of theNN, graphsof the input vector, the target vector
and the output vector).
The program that we are pursuing will be ready

for use in the biomechanics laboratory for study of
gait. The body model can be modified by changing
theweight of the body or of individual segments, from
which we can count the moments of inertia of these
segments and the forces in the joints. A more realis-
tic human model could be created, e.g. in CAD. In
the next step of our research we would like to pro-
duce a hydraulic mechanism that will be controlled
by NN and could help patients in their rehabilita-
tion.

4.1 Future developments

In the future we would like to build on our previous
work and develop our system.
The next useable function in the model will calcu-

late the load of the joint from the mass of the body.
Itmay be important to study duringwhichmovement
and in which part of the movement the joint is most
loaded by the mass.
Matlab Simulink offers a connectionwith theCAD

technical graphic program. Wewould like tomake use
of this connection and to recreate a real human body
in CAD. The animation will be more distinct in this
way.
Itwill takeaconsiderable time topredict theangles

in the knee and hip, and then to compute the cyclo-

grams. We aim to teachNN to predict the cyclograms
correctly.

Acknowledgement

This work was carried out at FBE CTU in frame of
research program No. MSM 6840770012 “Transdisci-
plinary Biomedical Engineering Research II” of CTU,
sponsored by the Ministry of Education, Youth and
Sports of the Czech Republic.

References
[1] Goswami, A.: Kinematic quantification of gait
symmetry based on bilateral cyclograms, XIXth
Congress of the International Society of Biome-
chanics (ISB), Dunedin, New Zealand, 2003.

[2] Goswami, A.: New Gait Parameterization Tech-
nique by Means of Cyclogram Moments: Applica-
tion to Human Slop Walking, Gait and Posture,
1998, p. 15–26.

[3] Heck, A., Holleman, A.: Walk like a mathemati-
cian: an example of authentic education, Proceed-
ings of ICTMT6 – New Technologies Publications,
2003, p. 380–387.

[4] Ju Won Lee, Gun Ki Lee: Gait Angle Prediction
for Lower Limb Orthotics and Prostheses Using
an EMG Signal and Neural Networks, Interna-
tional Journal of Control, Automation, and Sys-
tems, 2005, p. 152–158.

[5] Jelínek,R.: Tvorba modelu svalově-kosterního sys-
tému pro studijní účely, Kladno (Czech Republic),
2009.

[6] LUKOTRONIC [online].
URL: http://www.lukotronic.com/

About the authors

Ondřej HAJNÝ was born in Chomutov on
19. 11. 1987,where he graduated from the basic school
andthen fromthegrammarschool. He isnowstudying
at the Faculty of Biomedical Engineering of CTU, in
Kladno. He is employed as a teaching and research as-
sistant at the Institute of normal, pathology and clin-
ical physiology, Charles University in Prague.

BarboraFARKAŠOVÁwasborn inOstrava,where
she graduated from the basic school and then from the
grammar school. She is now studying at the Faculty
of Biomedical Engineering of CTU, in Kladno.

Ondřej Hajný
Barbora Farkašová
E-mail: hajnyond@fbmi.cvut.cz,
farkabar@fbmi.cvut.cz
Department of Biomedical Technology
Faculty of Biomedical Engineering
Czech Technical University in Prague
Nám. Śıtná 3105, 272 01 Kladno, Czech Republic

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