D4.13.3 3DRSBA Experiment Results and Evaluation v1.0

8/11/2019 D4.13.3 3DRSBA Experiment Results and Evaluation v1.0

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This document presents the final results of experiment 4.13 of the EXPERIMEDIA project.

Details are given of the procedure used in the project. As the results have been most promising,

further information is given of plans for exploitation and sustainability.

D4.13.3

3DRSBA Experiment Results and Evaluation

2014-09-26

Bertram Mller (3DRSBA)

Fredrik Mller (Qualisys)

www.experimedia.eu


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EXPERIMEDIA Dissemination level: PU

Copyright Qualisys and other members of the EXPERIMEDIA consortium 2014 2

Project acronym EXPERIMEDIA

Full title Experiments in live social and networked media experiences

Grant agreement number 287966

Funding scheme Large-scale Integrating Project (IP)Work programme topic Objective ICT-2011.1.6 Future Internet Research and

Experimentation (FIRE)

Project start date 2011-10-01

Project duration 36 months

Activity 4 Experimentation

Workpackage 4.13 EX13 - 3DRSBA: 3D Remote Sports Biomechanics Analysis

Deliverable lead organisation Qualisys

Authors Bertram Mller (3DRSBA)

Fredrik Mller (Qualisys)

Reviewers Stephen C. Phillips (IT Innovation)

Version 1.0

Status Final

Dissemination level PU: Public

Due date 2014-09-30

Delivery date 2014-09-26


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Table of Contents

1. Executive Summary ............................................................................................................................ 4

2. Introduction ........................................................................................................................................ 5

3. Experiment architecture and Implementation ............................................................................... 7

4. Results of experiments and conclusion ......................................................................................... 11

4.1. ACL screening tool ................................................................................................................. 11

4.1.1. Remote applications ........................................................................................................... 11

4.1.2. Automation .......................................................................................................................... 13

4.1.3. Scientific question ............................................................................................................... 15

4.2. Reporting and 3DCC .............................................................................................................. 18

4.3. Quality of Service and Quality of Experience .................................................................... 20

4.3.1. QTM ECC Client QoS metrics......................................................................................... 21

4.3.2. The eccQTMClient GUI ................................................................................................... 22

4.3.3. QoE ...................................................................................................................................... 24

5. General Conclusion .......................................................................................................................... 26

6. Exploitation ....................................................................................................................................... 27

7. Annexes .............................................................................................................................................. 28

Appendix A. Scientific model and descriptions.......................................................... 29

A.1. Marker position chart ........................................................................................ 29

A.2. Model information ............................................................................................. 30

A.3. Capture flowchart ............................................................................................... 31

A.4. Procedure Protocol ............................................................................................ 34

A.5. Excerpt of the report template for the Screening-PAF ................................ 35

Appendix B. Clinical questionnaire .............................................................................. 36

Appendix C. Original questionnaire for QoE ............................................................ 39C.1. Informative text .................................................................................................. 39

C.2. List of Question .................................................................................................. 39

Appendix D. ECC setup and programming ............................................................... 41

D.1. QoE and QoS integration in Babylon (Half-term situation) ....................... 41


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1. Executive Summary

This deliverable reports on the final status of the EXPERIMEDIA experiment of 3DRemote

Sports BiomechanicAnalysis (3DRSBA). It summarises the development of the project including

the information given in:

D4.13.1 - 3DRSBA Experiment Problem Statement and Requirements;

D4.13.2 - 3DRSBA Experiment Progress Report.

The results of the experiments has shown the feasibility of initial idea of the project, one which

has opted to a very high goal considering the given time frame. Within the dynamics of the

experiment, several approaches had been discarded and new ones developed up to the point that

even if further refinement is of interest for all participants, the approach investigated is already

implemented as a new tool and applied to other areas at the Experiment site, which is the high

performance centre (CAR) in St. Cugat/Barcelona - Spain.

The goal of 3DRSBA is to bring biomechanical analysis to the athlete in order to facilitate

screening for possible risk of lesions. It is based on the clinical need of injury prevention in athletes

associated with intense training routines. Due to the high impact of knee leasions to the career of

an athlete, the focus of the project was in the prevention of anterior cruciate ligament (ACL)

injuries.

The experiment is using modern technology used in biomechanical laboratories but transported

to new areas, such the training field, by using fast internet connections and modern multimedia

and remote control tools.

The experiment itself consists of three general components:

The feasibility to use remote control techniques in biomechanical analysis;

The clinical viability of this approach, including clients satisfaction;

The easier presentation of such complex biomechanical data to non-experts.

This documents lists the final results of the experiment and the future plans for exploitation. It

will explain the successful outcome of the experiment idea as well as important stages and findings

for the methodologies used.


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2. Introduction

3DRSBA stands for 3DRemote Sports BiomechanicsAnalysis, an experiment with the goal to

bring biomechanical analysis to the athlete in order to facilitate screening for possible risk of lesions

and enhance the prevention of those. ACL injuries might not have the highest prevalence in non-contact injuries, but the effect of it might be able to finish or seriously interrupt the career of an

athlete. As CAR as already a clinical prevention service in place using 2D screening techniques, the

project was intended to (a) improve the service tool using 3D techniques as well as (b) augmenting

the availability of this service to a wider athlete-population. As remote application of such a

biomechanical tool is limited to the mobile part of the system, which is the 3D camera system.

Other parts, which are integrated into the motion laboratory such as force plates, cannot partake

in the set-up. Therefore, the scientific feasibility had to be investigated as well. Only then can the

system be considered a valid clinical tool.

The structured analysis of motion has become a very important tool in sports analysis. Using newvideo technology, tactics and performance studies in team sports increases the performance and

general success. Besides the general motion studies, biomechanics intends to measure the motion

and relates it to the underlying musculoskeletal function. This provides information for the

performance of a single athlete, but can also provide information about the health status of the

individual regarding the musculoskeletal system. A very wide range of technologies for motion

capture are available, hence the outcome differs significantly with each system. Additionally, the

technique and analysis differs widely, depending on the methodology used. For example, animation

needs a continuously and steady signal, but does not need a physiological precision of the

individual. The latter would be essential for clinical science, where motion capture needs to be

complemented with other systems, such as force measurements and electromyography. This is

needed in order to not just measure motion, but to analyse and comprehend the reason for the

motion. This complexity is not just related to the technology applied, but also to the methodology

behind the analytics. This is known as the biomechanical model.

Whilst general motion analysis is used in the field, clinical motion analysis is more restricted to

specialised laboratories, which consist of complex technology and specialised professionals to

handle it. Currently, the professional education of such professionals is extensive and requires

many years of experience; hence the availability of such experts is limited. This results in one of

the main obstacles: the limited availability of biomechanically literate technical staff in addition totrainers; more so during busy periods of multiple training sessions in different locations at CAR.

Whilst the laboratory approach delivers results of high technical quality, with carefully controlled

parameters, it comes with some disadvantages. A full analysis session in the laboratory is an

interruptive event in the already tight schedule of an elite athlete with a professional training plan

and living and other educational requirements.

The proposed experiment will focus on bringing biomechanical screening techniques directly to

the training site of the athlete at CAR. The high number of athletes at CAR does not allow

monitoring and testing all of them and general biomedical services are limited. Notwithstandingthese concerns, the screening of musculo-skeletal performance has been widely suggested in sports


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science and yet, other than in a few exceptional cases, there is no widespread provision. By

bringing together these powerful technologies the motion tracker by Qualisys, control remote

technologies as well as the communication interface established at CAR under the auspices of

the EXPERIMEDIA project, one can expect a direct payoff in the Quality of Service (QoS)

provided by the institution, as well as Quality of Experience (QoE) for the athlete.


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3. Experiment architecture and Implementation

The experiment has to be divided into two different approaches being investigated: The first one

is the scientific question of feasibility of the 3D anterior cruciate ligament (ACL) screening, which

has followed a clear development path of the time of the experiment. The results will be detailedin chapter4.1 and4.2.The second on is the investigation about quality measurements for service

and users. This was a very dynamic development which changed approaches over the time of the

project. Thus, a great part of the achievements in this will be mentioned in the implementation

part of this deliverable and only the latest development will be shown in the results at chapter4.3.

Whilst the scientific question was well established from the beginning, the implementation of the

core components of EXPERIMEDIA had a more open status, as the details and possible

implementations had to be established at the experiment itself, not prior. Additionally, the core

components itself underwent further development over time. A third aspect of the project was the

circumstance that the complexity of the project would need implementation of 3rd party products

in order to maximise the efforts for the general project. The inclusion of the Teamviewer software

(Teamviewer GmbH/Germany) and Visual 3D software (C-Motion Inc./USA) had relieved the

experimenters on aspects of the project, but were dependent on software changes from both.

The original architecture intended to use can be seen inFigure 1,where CAPTURE and REMOTE

are part of the scientific component and EXPERIMEDIA was part of the ECC component.

Figure 1: Initial project architecture.


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In this structure, the QoS and the questionnaire for gaining an opinion of the athlete (Lime-Survey;

7.Appendix C)is separated. This would have had the advantage that athletes are independent of

time and machine (PC, mobile phone) to answer the questions. However, there would be no

guarantee that the questionnaire is filled out and a large loss of information was suspected.

Therefore, the integration of QoE and QoS was being proposed (Figure 2).

At this stage, the use of the Babylon infrastructure were considered to be the best option, as the

application is system independent (Windows, Linux, mobile apps). The essential metrics were

recognised and a Babylon structure was established (7.D.1). The deciding factor for not following

this approach were the limitation to one-dimensional data for questionnaires, which would have

been insufficient for gaining quality opinions.

The final structure (Figure 3) is the result of the development process and further detailed in

chapter 4.3.This included the development and programming of a virtual machine (Qualisys

Virtual Machine - QVM), which is integrating all aspects of the ECC. It has been developed with

the ability to be machine independent, meaning that it has not to be installed or run from the

biomechanical capture machine. It is operating system independent and able to pull or push data

to all clients or servers. The extra development of this ECC structure was needed, as the orientation

of the original core components were addressed to a wide population with parallel use of the

application as well as being shared to other participants in social media. The aim of this specific

experiment was interested to use the concepts presented for future internet in multimedia, but

with a focus on sequential application as well as the lesser interest of an individual athlete to share

Figure 2: Half-term structure of experiment architecture.


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clinical data within a social network. Sharing such experience would definitely be of use fordissemination of the screening technique. However, other communication programmes such as

twitter, Facebook etc are already fulfilling this task.

The scientific part included the (1) medical question of application and feasibility of 3D ACL-

screening, (2) the development of an automated structure in biomedical report generation as well

as (3) the investigation the utility in a professional surrounding, where performance in all aspects

is a daily necessity.

Point onestarted with a combined study by the biomechanical service at CAR and Qualisys about

current practice at CAR and state-of-the-art in the field. The fact that CAR provides a screeningservice for many years using a 2D video technique provided an excellent starting point, as the

usability of the experiment depends highly on a quality gain for the centre as well as the athletes.

The initial research including an extensive literature review into using a 3D technique for ACL

screening led to the point of starting to us a biomechanical model developed by the biomechanics

department of Liverpool's John Moore University. However, the limitation of this approach was

that this model was used in a scientifically controlled surrounding, with conditions quite differential

from a high performance centre. In the latter, the sole focus is to provide top-service to each

athlete, fully adapting to the individual needs and interests. As a model only defines marker-

placements (see7.A.1)and anatomical relations (see7.A.2), the practical and especially clinical use

needs to be established in a surrounding such as CAR.

Figure 3: Final structure for implementation.


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The development of the Project

Automated Framework (PAF) aspoint

two included an initial feasibility run for

similar settings and the planning of the

flowchart. Initially, an existing product

from Qualisys, called the Running PAF

(Figure 4), has been tested at CAR in

order to establish the requirements of

the Screening PAF. From this point, the

detailed requirements of the Screening

PAF could be established and the

Process structure (Figure 5)detailed.

The investigation for the best way of

reporting data started from thecommon method of biomechanical reporting, such as showing motion graphs. Measurement of

acceptance depended on the outcome of the PAF development, therefore being postponed

towards the third quarter of the experiment time.

The third pointhad been considered in the development of the other parts of the project, but

could only be tested and adapted once the PAF was functional. Included in those considerations

were the paper report sent to the requesting person, remote participation of all participants and

the preparation time to arrive to the conclusion. The results of this part is presented in chapter4.2.

Additional aspects in the implementation of the experiment related to ethical issues, privacy and

IT management of the data. This information has been established and followed as described in

the project deliverables 4.13.1 and 4.13.2.

Figure 4: Athlete being tested using the running PAF.

Figure 5: Shows the full 3D workflow processing the 3D data from image to analysis.


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4. Results of experiments and conclusion

4.1.

ACL screening tool

4.1.1.

Remote applicationsUsing the internet for remote control and participation in biomedical applications requires two

important parts: (a) The connectivity of all aspects for the participants and (b) special tools for the

experts to use and support the handling of specialised systems, such a 3D capture system. Such

remote application would require a huge amount of programming and development time. Prior to

presenting the experiment proposal, a market study was conducted and the software "teamviewer"

(Teamviewer GmbH/Germany) being considered to be best option for the requirements of the

project. The practicability for the experiments still had to be tested.

Such test consisted in two different aspects: The remote control function and the meeting function

(Figure 6). The remote control is needed for the ability of an expert of such systems (Biomechanist)

to provide support of 3D systems outside the laboratory. The meeting function would be needed

to share the results of the test or even allowing for participation with other partners, such as

coaches or medical personal. For such reasons, data encryption was essential for the usability of

the remote function. The Quality of the remote control was as expected and quickly implemented

in the general workflow of the PAF (Figure 7).

The meeting aspect of the software was also very sophisticated, resulting in the application not

just for the single experiment, but for video conferencing between all EXPERIMEDIA partners.

Figure 6: Teamviewer start screen for remote control or meeting function.


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The second remote aspect was the development of additional tools for the Biomechanist to have

direct access to the cameras and system, which would facilitate further the support when capturing.

For this reason, two applications were developed by Qualisys. The first one, "viewfinder" (Figure

8), allows the optical set-up of the specialised Cameras for 3D capture. As the complex settings

for optimal capture needs to be performed by a well-trained expert, the application allows the

expert not to be on side, but setting up the system remotely.

Figure 7: Teamviewer remote session with the motion laboratory.

Figure 8: Viewfinder application for tuning the camera system.


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The second application, QTM remote (Figure 9) allowed the capture system to be controlled

remotely for start/stop and event creation. This allows to operator to be away from the capture

PC.

An additional benefit was established with using the QTM remote and teamviewer together, as it

allowed the whole capture being performed by a single person, while the other one is away andstill not leaving out on essential tasks at capture.

Both applications can be run on iOS or Android either on smartphones or tablets. They have been

developed to work within the same network, which would allow the use within the whole centre

of CAR with biomedical side and all the different training sides.

4.1.2.

Automation

CAR is a high performance centre for athletes and required to work with similar high performance

in providing the different services to athletes from the own centres or clients worldwide. This

requires that all services and systems used needs to be highly productive. Thus, even as a scientificexperiments, a strong focus was given on overall productivity and maximum automation.

The baseline was an existing Process Automation Framework (PAF) package developed by

Qualisys. The tests with the Running-PAF showed the need for storing an increased number of

meta-data, such as medical data, anthropometrics or observations. This would avoid using

additional software for such data storage (e.g. a spreadsheet programme) and also allow for

additional ability for statistics and cross-analysis (Figure 10).

Additionally, the PAF will guide through the whole process of capture, processing and reporting.

Therefore, a clear structure needs to be established prior to programming as seen in7.A.3.Thisworkflow is then implemented into the PAF as seen inFigure 11.

Figure 9: QTM remote application.


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Project data tree:

Overall structure for all tests. Relates to the filestructure as well as the meta-data

Detail windows:

Contains all the meta-data required for the tests.

Can be fully adapted to the user's needs.

Figure 10: PAF control window: Project tree and meta-data information.

PAF Control:

Guide for capture sequence. Red field indicates

that trail has not been captured, might be optional

if the minimum number of trials has been

captured.

Green indicates captured trial. The mark on the

left indicated that this capture should be used forfurther analysis. Comments can be added at the

right side.

The GO-button will run through the whole

sequence. The Analyse button will prepare the

data for transmission, initiate the transmission and

proceed with the calculation and reporting in a

single step.

Figure 11: PAF control window: workflow guide.


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The PAF itself is implemented in the Capture software from Qualisys, called QTM. Once the data

are captured in QTM, the labelling of the markers will be started. In this step, the 3D points in

space will be named, which is needed for further biomechanical processing. This labelling is

automated by a Qualisys method called AIM. In this method and with prior training of the AIM

the markers are labelled automatically. The result can be checked and revised if necessary by the

expert. Guided by the PAF, the data will then be transmitted to the biomechanical software

package Visual 3D (C-Motion Inc./USA). This programme is currently the most versatile

biomechanical software package on the market and allows also for automation. Therefore the PAF

can control all procedures from the outside.

Once the data are transferred, the files are classified for motion pattern, side or any other criteria

(Figure 12)and then calculated using the model established for the project. As this processing is

not developed by this experiments rather than uses the possibilities of the software package, the

effects will be detailed in chapters4.1.3 and4.2.The automation will run till the report generation

is finished and can directly be printed, either electronically as PDF or on paper. It thereforeconcludes the data processing. However, the data gathered will need a profound analysis by experts

in order to produce a biomedical meaning.

The full automation has the effect, that the PAF works fully only when the original motion pattern

are used. For any other motion, different PAF's needs to be developed or the biomechanical expert

would have to produce the data in the currently common way.

4.1.3.

Scientific question

ACL screening is a hot-topic in biomedical sports science. The occurrence of such injuries are not

the largest number of lesions, but the impact of it is very high on each individual. The extended

time of treatment can seriously hinder or finish a career of a professional athlete. Therefore the

prevention of it is of importance, but the possibilities in detecting it is still limited. To apply a 3Dscreening tool and increase the use of in to a wide population is the backbone of the experiment.

Figure 12: Automated classification of capture files at Visual 3D using the PAF.


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It is a complex system containing aspects of scientific validity, practicality as well as general

feasibility.

Based on the experience at CAR

with 2D screening and an

investigation by Qualisys and CAR

of the current state of art in ACL

screening, an initial marker

placement protocol was established

(7.A.1 and 7.A.2). In parallel, the

general idea for the development of

a Process Automated Framework

(PAF) was tested at CAR for

concept verification. A PAF for

running analysis was tested, whichwas developed by Qualisys (Figure

13).

Based on those trials and the base information, a flowchart was established (7.A.3)for the process

of screening. Following further captures with athletes at CAR, the PAF has been developed and

applied (chapter4.1.2).

The feasibility of the concept was tested with participation of first and second Spanish league

players from RCD Espanyol (Figure 14). For about two weeks, up to 9 players were tested on a

single day.

The concept itself did work exceptionally well, but the necessary preparation time for each athlete

prior to the test and the analysis of the data had still a very large time factor for the biomechanist.

The increased performance in providing the screening service did not improve the personal

working quality, as the through-put increased. Especially the analysis part and reporting had a large

time factor.

As the concept itself had proven highly successful, the work on the Screening-PAF will continue.

Additionally, it has been already applied for different sports activity at CAR. Additionally, the

concept started to be applied not just for the screening, but for athlete training. The mobile setup

as well as the remote abilities allows the use in and outside the laboratory.

Besides the capture process, the biomechanical data needs to be modelled a calculated. For this

part, a 3rd party programme was used (Figure 15) for its flexibility and capability as well as

connectivity with the Qualisys system. This also allows to investigation into biomechanical sport

sciences at CAR.

Figure 13: Proof of concept test with running PAF.


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Figure 14: Running the Screening PAF with professional athletes.

Figure 15: Modelling and processing with Visual 3D (C-Motion Inc.)


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4.2.

Reporting and 3DCC

3D biomechanical studies do need an increased time for analysis compared with observational

comments. This is due to the complexity in the technology applied as well as the complexity in

concluding, as the motion performed by an athlete has always several possible explications, e.g. a

force discrepancy in a parallel jump could be due to a force deficit, pain, adaptation to a priorinjury, etc. As an effect, the best conclusion is the collaboration between the different professionals

supporting the athlete.

The biomechanist is still the professional capable to fully understand and comment on the graphs

captured. In order to arrive to the best recommendation, those information needs to be

transmitted to coach and clinical service. Currently, biomechanical reports includes several pages

of detailed capture information, usually divided into different motion planes and measured

articulations. This is also delivered in order to facilitate further comparison and supports the right

of each athlete to have access to all gathered information. However, it would be impossible to fully

comprehend each graph and in those environments, fast conclusions are appreciated. Therefore,two pathways were investigated: (a) the graph creating and delivering and (b) the information

exchange with other professionals.

The PAF itself is developed to produce the graphs in a fully automated way. Two report styles had

been tested, the first one consisting of all possible relevant graphs and the second and shorter one

as a selection of the most important information. A selection of the pages of the report is shown

inFigure 24 (7.A.5). This report contains measured and calculated data as well as information

about measurement quality in order to classify test validity.

Whilst very informative for the expert, the value of seeing those graphs for other professionals arelimited. At the end of the runs, the biomedical expert was individually preparing the documentation

for each athlete in order to provide the best condensed information possible. This will be a point

of investigation in the next step of the development.

Additionally, reporting the performed motion as an avatar was also investigated. The advantage of

it is the transmission of 'optical' data in a way coaches and medical personal is used to.

The modelling software comes with a data viewer which is freely available (Figure 17). However,

the use of the package is often too complicated in its use for non-biomechanist, which was also

observed in the analysis session for the tests. Therefore a simpler application was intended to beused. Using the avatar creation package of the 3DCC, an initial viewer was created (Figure 16).

First trials pointed out the details for further development and the possibility to use it

commercially.


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Figure 17: Original 3D viewer including skeleton motion and graphs.

Figure 16: Unity viewer for the drop jump motion pattern.


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4.3.

Quality of Service and Quality of Experience

The most important result of using QoS and QoE investigation was establishing the usefulness of

such a tool in biomechanical services. The development of the ECC client and the Qualisys virtual

machine had shown its potential and is already integrated in the development pipeline of Qualisys.

The handling of the system Quality of Service (QoS) and Quality of Experience (QoE) relates to

the Qualisys motion capture system that consists at its base of specialised motion capture cameras

(Oqus-cameras) and the specific 3D-reconstruction and tracking software called Qualisys Track

Manager (QTM). The PAF environment is an additional layer on top of this base.

The QoS is extracted from the QTM software and the transferred to the ECC server by using a

small broker program, the "eccQTMClient" that was developed during this project. The

architectural overview:

The ECC server can be run either locally on the same host computer as the QTM software orremotely on any machine accessible over the internet (access through IP-address and port

number).

For development and testing (but also possible to use for certain types of actual deployment) the

very convenient VirtualBox/Vagrant solution provided by the ECC development team was used.

The eccQTMClient was developed in C# using the .NET samples provided by the ECC

development team.

VBox

eccQTMClient.exe

QTM.exe

ECC

Figure 18: Architecture of Qualisys ECC.


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4.3.1.

QTM ECC Client QoS metrics

The QTM ECC Client metrics can be divided into the following groups:

Performance oriented metrics:

QTM physical memory usage

General performance related parameter showing how much physical memory is being used by

the QTM process.

QTM virtual memory usage

General performance related parameter showing how much virtual memory is being used by the

QTM process.

QTM CPU usage

General performance related parameter showing how much CPU time is being used by the QTMprocess.

Activity oriented metrics:

QTM event log

Storage of all events generated from the QTM software describing the different states QTM

transitions between. The following events are available:

Calibration started Connected

Calibration stopped Connection closed

Camera settings changed RT from file started

Capture fetching finished RT from file stopped

Capture saved Capture started

Capture stopped Waiting for trigger

Number of QTM calibrations

A count of how many calibrations of the system has taken place. A high frequency of

calibrations indicates a problematic measurement situation (but not in all cases).

Length of QTM calibrationsThe length in time (seconds) for a calibration to be performed. Longer calibrations indicates a

more complex measurement situation (but not in all cases) such as larger measurement volumes

and obscuring items.

Number of QTM captures

A count of many measurements has been done. An indicative value of productivity (a huge

count does not always mean high quality).

Length of QTM captures

The length in time (seconds) of the measurements performed. Indicative for the complexity of

the measurement but also the strain on the athlete being measured upon.


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Debug metrics:

These metrics has been used mainly as debug metrics during development. The key thing is that

these metrics do not require the connectivity of the camera hardware to the QTM software.

By using the "Run real-time processing on file" function in QTM a previously measured file can

be re-run exactly as if it was created in real-time using cameras.

Number of RT-on-Files

A count describing the number of times a file has been run using the "Run real-time processing

on file" function in QTM.

Length of RT-on-Files

The length in time (seconds) a file has been run in the "Run real-time processing on file" mode.

4.3.2.

The eccQTMClient GUI

The eccQTMClient GUI (Figure 19)has a couple of main areas. The QTM RT Server connection

shows the IP-address and port for the QTM server connection as read from the eccqtm.ini (Figure

20)file and the connection status.

The Rabbit Server connection shows the IP-address and port for the ECC server connection as

read from the eccqtm.ini file and the connection status.

Figure 19: QTM ECC client with status information and QoE input.


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The message log part shows debug information during the initialization and communication

between the QTM and ECC software. This information is also stored concurrently to a file called

log.txt.

When accessing the ECC dashboard through its web based GUI a sample view looks like this:

Figure 20: Sample of the eccqtm.ini file.

Figure 21: ECC dashboard sample.


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These graphs are updated as the system activities takes place and the aggregated log file can later

be retrieved by using the download function of the ECC dashboard.

The log.txt file looks like this showing initialization, metric generation and commands during

execution of the eccQTMClient:

4.3.3.

QoE

During the project several possibilities were available regarding the handling of QoE information.

An option that was considered was the possibility to use the eccQTMClient for this. When pressing

the QoE button in the eccQTMClient GUI, the following dialog box emerges:

Figure 22: Sample of log file information.


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This was however not fully implemented and it was considered better to use separate software in

order to handle this information.

Figure 23: QoE Questionnaire within the QTM ECC client.


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5. General Conclusion

The results of this experiment has had a direct and positive impact on the biomechanical services

applied at CAR. The developed concept of ACL screening is viable for all the different aspects:

Technology, scientific method and clinical tool. Together with the different remote tools thesystem can be more widely applied. In the current configuration, it already has become not just a

screening tool, but an application which will help to increase the performance of the athlete.

By investigating the different components of the EXPERIMEDIA ECC, the first interesting

outcome is the usability of this concept in sequential application of biomedical services, allowing

the performance of such services to be monitored and optimised. The data gathered in this

experiment also showed the need to careful analysis the numerical values. The full possibilities of

the ECC system will be further investigated by Qualisys.

Whilst the PAF performed precisely and rapidly, the transmission of this data still needs furtherdevelopment. This is due to the complex analytical method applied and the large amount of

numerical and graphical data collected. The transmission of this data has to be a good compromise

between full information and readability. Providing full information with motion graphs to other

professionals would require them to have special knowledge. Even if personnel are further trained

with this kind of analysis, the experiment showed the best results for the athlete when direct

communication (remotely or in person) was established between biomechanist and

coaches/medical personnel, instead of passing the motion graphs.

Further investigation is needed to properly understand the best technique for reporting the

findings, such as using motion viewers. In order to establish such criteria, some more applicationtime has to be given to the professionals in order for them to become more used to the system

and use it to its fullest potential.

The success of the experiment has been highlighted by the use of it by professional football teams:

screening so many athletes in such a short time was previously not possible.


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6. Exploitation

Three different direction of exploitation has been established at the end of the experiment:

Use of the scientific content of the experiment for general sports science, which willcontinue to be investigated and presented on symposia for application at other centres.

Both, CAR as well as Qualisys can profit from this aspect. Dissemination is expected to

be at scientific conferences.

The improved and additional service at CAR using the 3D system and remote

capabilities.

The new PAF developed will be included in the catalogue of products at Qualisys. Both,

CAR as well as Qualisys are able to profit from it. Results will also be shown at

presentations for clients and scientific meetings.

Besides those primary directions, the results of the data captured with this setup will help toimprove the clinical aspects of ACL screening. The combined availability of motion data and meta-

data allows for complex investigation in the future at CAR.


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7. Annexes

The following pages includes additional details about different aspects in the development of the

3DRSBA experiment.


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Appendix A. Scientific model and descriptions

A.1. Marker position chart

The marker placement at start has been taken from:

The intention was to be compatible with future developments, as those authors have a long

experience in ACL screening. Whilst not fitting all requirements at CAR, it was considered the best

compromise.


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A.2.

Model information

Besides markers placed on the surface of the human body, calculation has to be done to relate

surface point to internal anatomical landmarks which are:

THORAX_PROX: the midpoint between C7 and STERNUM THORAX_DIST: the midpoint between T8 and XIP_PROC

SHOUL_JC_L: offset from ACROM_L

SHOUL_JC_R: offset from ACROM_R

F_LHIP: functional hip joint left

F_RHIP: functional hip joint right

F_LKNEE: functional knee joint left

F_RKNEE: functional knee joint right

F_LKNEE_X: functional knee joint offset along functional knee axis left

F_RKNEE_X: functional knee joint offset along functional knee axis right LK: lateral knee joint marker projected onto functional knee axis

MK: medial knee joint marker projected onto functional knee axis

From all those points, segments are created, which are the rigid bodies the motion is calculated in

between. Those segments are:

Thorax (RTH): Proximal: THORAX_PROX + ACROM_R

Distal: THORAX_DIST

Tracking: C7 + STERNUM + XIP_PROC + T8

Pelvis (RPV): Proximal: ILCREST_L + ILCREST_R

Distal: GTROC_L + GTROC_R

Tracking: ASIS + PSIS + ILCREST

Upper Leg (LTH & RTH): Proximal: F_HIP

Distal: GTROC_L + GTROC_R

Tracking: Upper Leg cluster

Lower leg (LSK & RSK): Proximal: F_KNEE

Distal: MAL_MED + MAL_LAT

Tracking: Lower Leg cluster Foot (LFT & RFT): Proximal: MAL_MED + MAL_LAT

Distal: MTH1 + MTH5

Tracking: HEEL + MTH1 + MTH5 + MAL_LAT (+ MAL_MED


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A.3.

Capture flowchart


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A.4.

Procedure Protocol

The protocol was intended as a procedure checklist when doing the experiments. It was used in

the original captures until most of it has been integrated in the PAF as Meta-data.


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A.5.

Excerpt of the report template for the Screening-PAF

Figure 24: Selection of pages from the report generation.


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Appendix B. Clinical questionnaire

In order to gain more relation between motion pattern and physical condition of the athlete, the

Knee injury and Osteoarthritis Outcome Score (KOOS) has been implemented. It is a four-page

questionnaire for gathering the clinical status of an athlete previously injured within the last 14days prior to the tests.

The questionnaire was originally implemented in Lime-Survey, but finally discarded, as the

information is not only for gathering of metrics and statistical analysis, but also for medical analysis.

In the latter function it would need to be part of the medical history of an athlete and the print-

out capability of Lime-Survey were not sufficient, as seen inFigure 25: Exert of Lime Survey print-

out (Spanish Version). For this reason, a PDF form has been produced in English and Spanish

(Figure 26).


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Figure 25: Exert of Lime Survey print-out (Spanish Version).


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Figure 26: Exert from the PFD formulary of the English KOOS.


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Appendix C. Original questionnaire for QoE

The questionnaire is intended for the athletes in the screening tests and the coaches.

When a screening test is performed, each participant (athlete/Coach) will be given a full witha reference number and the internet address for the survey. This reference number will stored

with the capture data in order to be available for improving the screening process itself, based

on the feedback given in the survey. The reference number will be deleted at the end of

EXPERIMEDIA.

The survey itself is anonymous as well as any statistics applied to it.

The answers will be rated by:

1 Unsatisfactory

2 Poor 3 Acceptable

4 Good

5 Excellent

The interest of the questionnaire is to establish the usefulness of the screening test from the

athlete's point of view, including the benefits explained by the professional. It should provide

information about the structure of the test, the impact to the training schedule, the general use

in biomechanical service in order to compare with the current situation.

C.1.

Informative text

This survey is part of the EXPERIMEDIA project 3DRSBA, held at CAR. The reference

number you were given at the screening serves for improving the service at CAR directly.

The questionnaire and all your responses will not be linked to any personal information about

you or being used for any profiling intentions of you. The sole intention of this survey is to

determine the quality of the project as described in the documentation at

http://www.experimedia.eu/.

Please answer the questions by using 1 as the worst and 5 as the best qualification.

C.2.

List of Question

1) Which type of sport are you training in? (Text or fields)

2) For how many years are you active in your sport? (Field)

3) Was the EXPERIMEDIA (including the test) and its purpose explained to you? (1-5)

4) Was the explanation of the project to your satisfaction? (1-5)

5) Did the experiment and the explanations match? (1-5)

6) If not, what has been different: (Text)

7) Do you think that this type of screening-test would be beneficial to your sport? (1-5)

8) Do you think that this type of screening-test would improve the clinical service given at

CAR? (1-5)9) Do you think that this type of screening-test would beneficial to you personally? (1-5)
http://www.experimedia.eu/http://www.experimedia.eu/


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10)Did you receive any biomechanical service before? (Yes/No)

11)If yes, how often per year? (1, 2, 3, more than 3)

12)Is the duration of the test acceptable? (1-5)

13)Was the warm-up time sufficient for you to be prepared? (Yes/No)

14)Did you find the test tiring for you? (Yes/No)

15)

Did the test had negative influence to your training schedule? (Yes/No)

16)If yes, what would you change? (Text)

17)Do you believe that regular screening would help you in the process of preventing

injuries? (1-5)

18)Any other remark you would like? (Text)


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Appendix D. ECC setup and programming

D.1. QoE and QoS integration in Babylon (Half-term situation)


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D4.13.3 3DRSBA Experiment Results and Evaluation v1.0

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