3D Interactive Environment Applied to Fencing Training

Carla P. Guimarães

, Vitor Balbio

, Gloria L. Cid

, Maria Isabel V.Orselli

, Ana Paula Xavier

Augusto Siqueira Neto

and Sônia C. Corrêa

National Institute of Technology, Rio de Janeiro, Brazil

LACEM, Presbyterian University Mackenzie, São Paulo, Brazil

Franciscan University, Rio Grande do Sul, Brazil

Keywords: 3D Digital Platform, Biomechanics, Eye Tracking, Fencing, Training.

Abstract: The purpose of this study was to present a 3D interactive environment - a Digital Platform to help in fencing

training. The first fencing motion described and analysed at the 3D platform was lunge in epee fencing. The

platform was able to show kinematic variables of upper and lower limbs and the center of mass that

characterized a good performance in epee fencing. The platform also incorporates a digital database of eye

track motions of the fencers. An OptiTrack motion capture system was used to capture the lunge motion of

five skilled amateur fencing athletes in the presence or not of a static opponent and an Eye Track System

Tobbi II was used to track the eye movements of the fencers when performing a lunge attack with a target.

The 3D platform was developed using Unity3D and can present some interesting results to improve

available information to coaches. That highlights the importance of visualization biomechanical results

based on coach criteria in a more understandable way to help athletic training.

1 INTRODUCTION

It is interesting to appoint that there is a big gap

between scientific knowledge and coaches. It

happens all over the world. There is a lot of

knowledge being developed in the labs but it does

not reach the coaches. The languages are very

different. The coach usually does not understand the

graphics and data that the biomechanics researchers

generate and the coaches need something that they

really can use in their daily practice. We intend to

work in this gap, trying to fulfill the coaches’ needs

but bringing the precision and reliable data that we

can provide.

The use of interactive system is interesting and can

fill this gap communication between coaches’ and

researchers. The Ergonomic Laboratory researchers

of National Institute of Technology (Guimarães et

al, 2015) have been working in this approach in

other projects that involve combat sports as Jiu-jitu

and also in ergonomic study applied to education

and training of caregivers.

In general, publications of biomechanical parameters

applied to fencing and martial arts are reduced (Roi

and Bianchedi, 2008, Correia and Franchini, 2010).

The consequence is the lack of specific knowledge

to give support to teaching and coaching in these

sports. Another important parameter that on which

there is a lack of studies is the gaze behavior, which

can be used to identify search strategies and

differences between skilled and less-skilled athletes

(Oliveira, et al, 2008). The lack of access of

teachers and coaches to the human movement

science laboratories can be a possible reason.

Based on this scenery the purpose of this study is to

develop a 3D interactive environment and platform

to help in fencing training, which also incorporate

the identification of the most common human body

structures focused by high-level fencing athletes

during their practice.

The first selected fencing motion to be studied was

lunge because it’s the basis of most attacking

motions and one of the first movements to be

learned in fencing. We studies this in the presence

and absence of a target to be hit.

2 METHODOLOGY OF

DEVELOPMENT

In this session it will be presented the methodology

Guimaraes, C., Balbio, V., Cid, G., Orselli, M., Xavier, A., neto, A. and Corrêa, S.

3D Interactive Environment Applied to Fencing Training.

DOI: 10.5220/0006043100390043

In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2016), pages 39-43

ISBN: 978-989-758-205-9

of data acquisition and software development used

in this project.

2.1 Motion Capture

For this project we evaluated 5 skilled epee's fencing

athletes (3 female and 2 male, four of them (2 male

and 2 female) were part of the Brazilian Olympic

Team in 2016. The subjects perform a lunge attack

at their best, from a static en garde position in two

different experimental conditions: first, without the

presence of any target to be hit and second, having

their coach’s chest as the target. For each condition,

the task was repeated at least four times. Before data

acquisition, the athletes had a time to get used to the

task, which included, for the second condition, find

the proper athlete to coach distance.

The athletes’ whole body motion was captured

with an eighteen-camera optoelectronic system

(Prime 13, Optitrack, 240Hz sampling frequency) by

placing retro-reflective markers in anatomical

landmarks at their legs, arms, pelvis, trunk and head.

After each repetition, the coach qualitatively

evaluated the athlete performance according to his

own criteria and corrected the gesture if necessary.

Motive software (Optitrack, version 1.8 and 1.10,)

was used for motion capture, reconstruction and

preliminary data processing (namely, fill trajectory

gap through cubic spline interpolation, in case of

marker occlusion). (Figure 1).

Figure 1: Lunge motion captured using Optitrack system.

Data from the best-executed lunge attack of each

athlete in each condition, as judged by the coach,

was selected for inclusion in the digital platform.

Body pose during the lunge attack period was, thus,

exported, for animation purposes, using Biovision

Hierarchy format (bvh). In addition, the tri-

dimensional coordinate of each retroreflective

marker in the corresponding period, was exported in

c3d format, for kinematic analysis purposes. All the

relevant kinematic quantities calculation, as well as

the necessary data processing, were done with the

Visual 3D software (5.01 version, C-Motion). The

variables selected for analysis were based on the

criteria used by the coaches to judge the athlete

performance (Correa et al, 2015).

We used the Calibrated Anatomical System

Technique (CAST; Cappozzo, 1995) to calculate the

body segments instantaneous position and

orientation. The 3D joint rotations (joint angles)

were computed via Euler angles using the Cardan

sequence (flexion-extension, abduction-adduction,

axial rotation). Inertial characteristics of each body

segment were estimated according to the Zatsiorsky-

Seluyanov model modified by deLeva (deLeva,

1996).

The following variables were selected to analyse

the lunge: the foot angle relative to the anterior-

posterior direction (toe in-out angle); the angle

between the longitudinal axis of both feet; base

length and width; horizontal position of the centre of

mass (CM) relative to the unarmed (back) heel; each

segment, as well as whole body, CM displacement

and velocity in the forward, vertical and lateral

directions; the 3D angular displacements and

velocities of the upper and lower limb joints for

both, armed and unarmed, sides; pelvis and trunk

angular motion in the sagittal plane. The time series

of those variables were filtered using a 4th order,

zero leg, low-pass Butterworth filter, with a 6 Hz cut

off frequency. (Klauck and Hassan, 1998).

The digital platform allows the user to visualize

the time series and instantaneous values of

biomechanical variables, by selecting the

corresponding joint or segment at the movement

animation. At the present, the following variables

are allowed for visualization: ankle, knee, hip, wrist,

elbow and shoulder joint angle at the frontal

(abduction-adduction) and sagittal plane (flexion-

Figure 2: Whole body center of mass (CM) displacement

and velocity in the forward, vertical (upward positive) and

lateral (unarmed side, positive) directions, during a lunge

attack without the presence of any target to be hit, at the

best performance of each one of the five athletes analyzed.

Vertical lines indicate the instant at which CM achieved

the higher forward velocity.

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

Figure 3: Joint angle in the sagittal plane for the armed

side and unarmed side lower limb joints, during a lunge

attack without the presence of any target to be hit, at the

best performance of each one of the five athletes analyzed.

An increase in joint angle means joint flexion. Vertical

lines indicate the instant at which the athlete’s CM

achieved the higher forward velocity.

Figure 4: Joint angle in the sagittal plane (shoulder and

elbow) and frontal plane (shoulder) for two of the armed

side and unarmed side upper arm joints, during a lunge

attack without the presence of any target to be hit, at the

best performance of each one of the five athletes analyzed.

An increase in joint angle means joint flexion and

abduction, respectively. Vertical lines indicate the instant

at which CM achieved the higher forward velocity.

Figure 5: Foot and Hand center of mass (CM) velocity in

the forward and vertical (upward positive) diraction,

during a lunge attack without the presence of any target to

be hit, at the best performance of each one of the five

athletes analyzed. Vertical lines indicate the instant at

which CM achieved the higher forward velocity.

extension), as well as the CM linear displacement

and velocity for the whole body, pelvis, trunk, head,

upper and lower arms, hands, feet, shanks and

thighs. An example of the data feeding the platform

can be seen in the figures. The data for the 5 athletes

(A1-A5) are shown together just for illustration

purposes. (Figure 1,2,3,4,5).

2.2 Visual Tracking Collection

The same fencing athletics were evaluated with

regard to their gaze behavior during their training

practices. For that, the athletes performed their

training section using an eyetracking (TobiiGlasses

2) under their protective masks. This device has 4

cameras to monitor the retinal movements and a

camera that records the images seen by the subject.

Data were analyzed with the TobiiGlassesAnalyzer

(version 1.16). (Figure 6).

Figure 6: Athletes using Eye tracking device.

Three main areas of interest (AOI) were identified in

the subject field of view, which were: the opponent's

Figure 7: Colour mapping that shows the areas of interest.

3D Interactive Environment Applied to Fencing Training

arm, trunk and face (mask). The frequency the

athletes focused on each AOI were calculated.

(Figure 7).

The results of these steps were inserted in the 3D

Digital Platform or 3D Interactive Environment.

3 PLATFORM DEVELOPMENT

The Virtual Platform was developed using mostly

“game technologies” since they are currently the

best available tools for virtual scenes 3D. The core

system was based in Unity3D (www.unity3D.com)

with addition of some add-ons such NGUI

(http://www.tasharen.com/) and others. The system

was split in modules to better organization of the

development and it components are presented in

Figure 8.

Figure 8: System Module Diagram.

 Core System: Is the main framework where all

the systems are integrated. It includes menus, 3D

viewport, graphs, skeleton reconstructions and

other interfaces.

 Animation System: Present the captured

animation using a skeleton reference where you

can select bones or joints.

 Graph System: Synchronized with the

animations we present some graphs related to

the captured movements. This include Bone

Rotation, Position and others.

 Eyetrack Database: Here, the Eye Track data

collected were made available.

The interface of the system was planned to be

easy to use and understand, an important feature

since mostly of the users may not have fluency in

digital applications. In the Figure 9 you can see the

main interface of the Mobile version, the desktop

layout is yet in development.

During the development was implemented a

process to insert all the data in the platform, it

follows some tasks:

 The model captured in BVH format is imported

in Blender (www.blender.org) (Figure 10)

 It animations are converted to a metric scale of

0.001, that way the model gets 1:1 scale when

imported in Unity.

Figure 9: Main Interface of the mobile version.

 All the animation poses are cleaned for wrong

keyframes and the correct segment that

represents the movement of interest.

 All the segments (bones and joints) are renamed

for a correct standard that we can read in the

platform. It’s used to the skeleton

reconstruction.

 All the data are imported and converted to a

XML file. That way we can read it in the

platform and plot the Graphs.

 The model is exported as FBX format that can

be read by the platform

Figure 10: Clean-up and Name Fixes Process in Blender.

3.1 Graph System

The Graph System shows selected data from

elements of interest of the model. It can be a data of

a Segment, a Joint or not necessarily associated to a

skeleton element as a gravity center e.g. the figure 4

shows the Desktop version of the platform using the

Graph system. (Figure 11).

Core

System

Animation

System

Graph

System

EyeTrack

Database

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

Figure 11: Desktop Platform with the Graph System.

1. To select the Movement to Analyze

2. To select the Athlete

3. To select the Element to Analyze

4. To select the Data to show the Graph

5. To select options as Axis and Scale to Show

3.2 EyeTrack Database

Inside the EyeTrack Database we can find all the

data collect from the eyetrack system of the athletes.

It commonly contains 3 files:

 .tsv file with the RAW data of the Eyetrack

 .xlsx file with a more accessible data that can

be read in Excel

 .mp4 video with the Eyetrack mirroring what

the athlete sees during the execution of the

movement

The image above shows the usage of the

eyetrack database. (Figure 12).

Figure 12: Eye Tracking Usage.

4 CONCLUSIONS

The 3D digital platform is an attempt to approach

fencing coaches and researchers in order to explore

biomechanical factors and visual aspects that may

lead to an improvement on epee fencing technique,

as well as in teaching and training methods. Its

support to mobile and desktop highlights the

importance of visualization and interaction of the

coach with results on the biomechanical parameters

and visual search strategies in a more understandable

and relevant way to training.

Based on coach's criteria, it was identified the

importance of the upper body and center of mass

kinematic variables to lead to a good lunge

performance. Another aspect of the research is the

identification of what is the most relevant visual

information to predict the movement of the epee and

opponent.

ACKNOWLEDGEMENTS

Acknowledgment: CNPQ is the sponsor agency of

this research

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3D Interactive Environment Applied to Fencing Training