2Trax3: Raising Accessibility and Everyday Use of Automatic Motion

Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation

Algorithms

Samir Duvelek

, Dominik Hoelbling

1 a

, René Baranyi

1 b

, Roland Breiteneder

, Karl Pinter

1 c

and Thomas Grechenig

1,2

Research Group for Industrial Software (INSO), Vienna University of Technology, Vienna, Austria

RISE Institute of Technology, Sri Sathya Sai District, Andhra Pradesh, India

Keywords:

Motion Capturing, Video Analysis, Kinematic, Martial Arts, Kicking Techniques, Artiﬁcial Intelligence.

Abstract:

A sound technique forms the fundamental basis for many sports, particularly Martial Arts, as it often distin-

guishes between successful hits and being hit. However, the process of improving one’s technique is highly

intricate, often requiring expert feedback and expensive technology such as 3D motion capturing. The integra-

tion of automated technique analysis has the potential to streamline this process and make it more accessible.

In this study, the aim is to democratize technique analysis by developing and evaluating a web application.

This application allows users to upload 2D video recordings of themselves performing the double side kick

technique and receive immediate feedback. To validate the analysis generated by the application, it was com-

pared to a Vicon motion app 3D analysis of the same data from a preliminary study involving 44 participants.

The results of Bland-Altman plot analysis demonstrated a highly signiﬁcant agreement between the 3D and

2D performance indicators (Mean differences: relative phase duration: <0.04s; vector spreading angle: <15

degrees; relative body position <13%), indicating that the web application is a suitable tool for fast and effec-

tive motion analysis.

1 INTRODUCTION

High performance in sports particularly requires ex-

tensive technique skills among other abilities. Es-

pecially, in combat sports, technique execution does

not solely serve to reach a biomechanical goal but

rather has a tactical purpose (Hoelbling et al., 2021b).

Studying your opponents and understanding their

strengths and weaknesses in advance is also essen-

tial. Once the opponent’s technique has been analysed

correctly, a counter-strategy can be applied (Ouergui

et al., 2021).

Furthermore it can also be used for self improve-

ment, a technique analysis enables targeted training

planning. Certain weaknesses, such as the execution

of certain kicks, can be eliminated through analysis

and subsequent targeted training.

The analysis of such sports techniques has been

the focus of biomechanical and kinematic research

https://orcid.org/0000-0001-7099-2576

https://orcid.org/0000-0002-0088-9140

https://orcid.org/0000-0002-8930-1875

for decades (Elliott, 1999). Even AI applications

continually gain popularity in the ﬁeld (Lapham and

Bartlett, 1995).

Technique analysis in combat sports is based on

a precise study of techniques, such as punches and

kicks. Each technique requires accurate execution of

sequential actions in combination with correct posture

and precise targeting to achieve maximum effective-

ness (Ambro

zy et al., 2020). Careful analysis and po-

tential adaption can be greatly improve the skills and

abilities of athletes by uncovering physical and coor-

dinative weak spots, which can be compensated in the

training process.

However, despite the multitude of advantages of

technique analysis, it requires highly professionalized

performers and is a very time-consuming task. Partic-

ularly, when it comes to complex motions, which are

rapidly executed with quite little deviations leading to

the distinction between success and failure in ﬁght-

ing situations, a complex multidimensional approach

is necessary to comprehensively disclose the optimum

and deﬁne the necessary adaptions (Lees, 2002).

128

Duvelek, S., Hoelbling, D., Baranyi, R., Breiteneder, R., Pinter, K. and Grechenig, T.

2Trax3: Raising Accessibility and Everyday Use of Automatic Motion Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation Algorithms.

DOI: 10.5220/0012165200003587

In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2023), pages 128-135

ISBN: 978-989-758-673-6; ISSN: 2184-3201

1.1 Double Side Kick

A number of current publications of the double side

kick in pointﬁghting kickboxing (Yuncza, 2011) for

example shows how complex this process really is

and how many factors must be taken into account to

model high performance (Hölbling et al., 2017). As

a gold standard of this type of analysis researchers

use i.e. Vicon 3D motion capturing systems (Barris

and Button, 2008). Late research successfully de-

ﬁnes a number of characteristics, strongly correlat-

ing with performance, measured by both, expert rat-

ings and general competition success (Hölbling et al.,

2020a) (Hoelbling et al., 2021b). However, extract-

ing these variables is practically impossible for regu-

lar coaches, as it requires a very high skill set, very

expensive 3D motion capturing technology and a suf-

ﬁcient amount of time. In addition, cameras can pick

up more detailed characteristics than visible to the hu-

man eye (Barris and Button, 2008).

Some of the study’s exploring the double side kick

used 3D cameras to record various athletes perform-

ing the technique. The technique was then semi-

automatically analysed using the 3D video recordings

(Hölbling et al., 2020b). The performances were si-

multaneously recorded using 2D cameras.

1.2 Preexisting Dataset

A comprehensive data set from a preliminary study

was provided as the foundation for this software ap-

plication (Hölbling et al., 2017). The experiment

recordings consisted of 3D Vicon motion capturing

data with corresponding 2D videos of the double side

kick executions of 44 athletes. All results of the 3D

motion capturing, were extracted and published by

previous researchers (Hölbling et al., 2017) (Hölbling

et al., 2020a) (Hölbling et al., 2020a).

1.3 Automated Analysis Using 2D Video

Given the opportunity of existing scientiﬁcally proven

performance characteristics in combination with cur-

rent advancement in the ﬁeld of AI expert systems

(Zhang and Lu, 2021) and user-based development

(Zorzetti et al., 2022) it appears possible to simplify

this process in a way that regular coaches and athletes

can easily track their performance status and progress.

In particular, these components could be used to de-

velop a system to automatically analyse such tech-

niques with known performance characteristics using

2D videos, advanced motion capturing algorithms and

a set of additional rules to signiﬁcantly decrease anal-

ysis time effort and sophisticated equipment. There

are already studies that compare the use of 3D and

2D motion capturing in other sports, such as cross-

country running (Maykut et al., 2015) and youth base-

ball (DeFroda et al., 2021), but no research for auto-

matic or semi-automatic analysis of Martial Arts tech-

niques was found.

1.4 Aims

For the purpose of simplifying complex analysis and

ultimately allow for integration of these methods into

regular training, the aim of this study is to develop and

evaluate a system for automatic analysis of 2D videos

of the double side kick, based on performance char-

acteristics deﬁned in (Hölbling et al., 2017) (Hölbling

et al., 2020a) (Hölbling et al., 2020b).

1.5 Hypothesis

Based on existing raw data from the fundamental pub-

lications, following hypothesis can be deﬁned. There

is a signiﬁcant agreement between the following per-

formance characteristics extracted in 2D (by the sys-

tem) and 3D (by the fundamental research) kinematic

analysis: (a) the duration of a speciﬁc segment of the

technique execution relative to the total execution du-

ration, (b) the vertical knee height at speciﬁc points

of the technique execution relative to the height of

the trochanter major in neutral standing position, (c)

the distance between knee and frontal shoulder at spe-

ciﬁc points of the technique execution relative to the

same distance in neutral standing position, (d) the an-

gle created by the hip and knee of the standing leg

with the vector connecting the knee and hip of the

kicking leg at speciﬁc points of the technique execu-

tion, (e) the angle created by the hip and heel of the

standing leg with the vector connecting the heel and

hip of the kicking leg at speciﬁc points of the tech-

nique execution, (f) the velocity of the vertical knee

elevation over the course of a speciﬁc segment of the

technique execution, (g) the velocity of the kick leg

over the course of a speciﬁc segment of the technique

execution.

2 METHODS

Requirements Engineering: In a ﬁrst step after liter-

ature research and qualitative dataset analysis, semi-

structured interviews (Adams, 2015) with domain ex-

perts in pointﬁghting kickboxing and biomechanics

were conducted and analysed via qualitative content

analyses (Mayring et al., 2004), to extract essential

requirements.

2Trax3: Raising Accessibility and Everyday Use of Automatic Motion Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation

Algorithms

129

Table 1: Node and phases of the double side kick.

Nodes

Description

Node 1 - Initialisation (INI)

Deﬁned as the moment when the

kicking legs’ foot loses contact with the ground

Phase 1 - Chambering 1 (CH1)

Consists of ﬂexion of the knee as well as ﬂexion and abduction

of the hip joint of the kicking leg

Node 2 - Time of measurement 1 (MT1)

Deﬁned as the highest elevation

of the knee before the kicking legs’ knee angle surpasses 110°

Phase 2 - Kicking phase 1 (KI1)

Mainly consists of extension of

knee and hip joint with ankle ﬂexion of the kick leg

Node 3 - Knee extension maximum 1 (KE1)

The ﬁrst kick ends with the maximum

extension of the kick leg’s knee

Phase 3 - Chambering 2 (CH2)

Re-Chambering is similarly deﬁned as CH1

and describes the preparation for the second kick

Node 4 - Time of measurement 2 (MT2)

Similarily deﬁned as MT1,

but after the ﬁrst kick

Phase 4 - Kicking phase 2 (KI2)

Second kicking Phase,

with the same deﬁnition as KI1

Node 5 - Knee extension maximum 2 (KE2)

Maximum extension of the

kicking legs’ knee and hip, leading to target contact

These requirements were later adapted and

extended based on observations and comments

throughout the development.

System Design and Implementation: Based on the

extracted requirements a frontend and backend archi-

tecture was designed and an application was devel-

oped using the method of prototyping (Floyd, 1984).

This procedure allowed practical demonstration of

relevant parts of the system early on in the develop-

ment process. Multiple iterations, incorporating ex-

pert feedback and trial and error processes were per-

formed, to improve user experience and measurement

accuracy.

Phase and Node Deﬁnition: The foundation for

motion analysis is the segmentation (Meinel and

Schnabel, 2007; Göhner, 1992) of the double side

kick into six functional phases and seven nodes as

deﬁned by Hölbling et al. (Hölbling et al., 2017).

Nodes are deﬁned as the exact moments when one

phase transitions into another, see table Table 1.

Variable Deﬁnition: After the deﬁnition and

segmentation of the movement, the performance

indicators had to be extracted and calculated at the

nodes or within the phases, as described in (Hölbling

et al., 2017). They are grouped into the following

three categories: (a) the relative duration of the

functional phases, (Hölbling et al., 2017) (b) the

relative position of relevant body parts (kicking legs’

knee height and distance between knee and front

shoulder) , (c) the accumulated angles between the

legs at the time of a node, (Hölbling et al., 2020a)

and (d) the velocity of a body parts motion during

a phase (Hölbling et al., 2020b), see table Table 2.

The time-domain parameter values in the following

text are the absolute duration given in seconds.

The angles are given in degrees. And the distance

parameter values are given as relative values without

unit.

Phase Detection and Motion Analysis Algorithms:

Based on the phase, node and performance indicator

deﬁnitions, an advanced algorithm was developed,

which solely relied on some anthropometric data

and was able to automatically separate the phases

and extract the variables. In an iterative process, the

algorithm was then improved.

Statistical Evaluation: In a ﬁnal step, the extracted

data (from the 2D analysis) was statistically compared

to the pre-existing data (from Vicon 3D motion cap-

turing). Statistical analysis and plot generation was

conducted using the Python programming language

Python Software Foundation. Python Language Ref-

erence, version 3.9).The differences between 3D and

2D variables were checked for normal distribution by

using Shapiro-Wilk tests. Similar to previous stud-

ies exploring the agreement between 3D and 2D mea-

surements in kinematic analysis (Peebles et al., 2021;

Schurr et al., 2017), Bland-Altman plots (Bland and

Altman, 1986) were calculated for each of the de-

pendent variables, in order to evaluate agreement be-

tween 3D and 2D analysis.

The y axis is constructed using the average mean

difference, because the x displays a small concentra-

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

130

Table 2: Performance indicators.

Durations

Relative duration of each phase,

normalized by the duration from the nodes INI to KE2

Distances

(i) The vertical height of the kick leg’s knee normalized by

trochanter major height in straight stance (KHK).

(ii) The relative distance between the kick leg’s knee and nearest shoulder normalized

by their distance in neutral standing position. Both extracted at every node

Vector spreading Angle (VSA)

(i) The angle between the femur bones of

both legs (vector connecting knee and hips’ center of rotation).

(ii) The angle between both legs (vector connecting hip and ankles’ center of rotation)

Velocity

The mean velocity of the kick leg’s knee vertical elevation during CH1 and CH2.

And the mean velocity of the kick legs’ foot in target direction during phase KI2

tion range (Bland and Altman, 1999). For 95% limits

of agreement were used.

For the angle characteristics the a priori accept-

able limits were set to 20

◦

. This decision was made

by consulting the results of similar studies where a

subset of the results exceeds 20

◦

(Schurr et al., 2017)

or where an average of 20

◦

agreement (Remedios and

Fischer, 2021) was reported.

For the duration and distance characteristics there

were no comparable studies found, therefore it has

been decided to calculate an equivalent of the 20

◦

limits of agreement taken from (Remedios and Fis-

cher, 2021). It was decided to consider the minimum

and maximum value for each of the individual charac-

teristics and choose the 25 % value of the difference

between minimum and maximum as the acceptable

limit for that characteristic. This choice was made to

ensure that in the case that, the 3D analysis measures

a performer to be either in the top 25 % or bottom 25

% of a characteristic, the 2D analysis agrees with the

3D analysis in so far that it puts the performer into the

top half or bottom half for that characteristic respec-

tively. This is deﬁned as the minimum viable case for

a characteristic to be considered in the 2D analysis.

This means for the duration variables the limits

are for CH1 (0.162s), KI1 (0.05s), CH2 (0.19s), KI2

(0.108s).

And for the distance variables: For KHK1 (22),

KI1 (28.5), KHK2 (16.5), DKS1 (15.5), DKS2 (20)

3 RESULTS

The gathered results will be described in the following

subsections.

3.1 Requirements List

The iterative requirements engineering process re-

sulted in a set of requirements, which are categorized

into the following three groups: (a) User instruction.

The user shall be instructed in how to execute the

technique and how to ﬁlm the technique being per-

formed. (b) User input. The user shall be enabled

to upload the video of the performance and metadata

that is necessary for the analysis. (c) Performance

feedback. The user shall be presented with feedback

about their performance.

3.2 Technical Design of the Application

The application is deployed inside of Microsoft’s

Azure cloud environment (Microsoft, 2023a) and uses

Azure resources for storing data (Azure blob storage)

and orchestrating the analysis (Azure container in-

stances).

The two backend services that process the input

video and calculate the analysis results are written

in Python. This was chosen, because the initial

processing of the input video is done using the

Python version of the Openpose system (Cao et al.,

2017).

Deployment: The chosen modular design of the ar-

chitecture allowed for custom deployment strategies

ﬁtting the needs of the individual parts of the system

(Salah et al., 2016).

The python services were deployed inside short-

lived docker containers. This was done, because of

the high hardware requirements for the Openpose sys-

tem (Openpose, 2023), and this way the usage of the

hardware is limited to the time the service is running.

3.3 User Instructions

Depicted in Figure 1 is the examplary analysis that

is presented to the user when the application is ﬁrst

opened. This provides the user with an insight how

the performance should be recorded and how the

technique should be executed. The performer in

the video is obfuscated and the analysis results are

blurred due to data privacy and ethical reasons.

2Trax3: Raising Accessibility and Everyday Use of Automatic Motion Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation

Algorithms

131

Figure 1: Analysis example presented to the user.

3.4 User Input

Figure 2 illustrates the interface where users can up-

load videos of performances. These videos capture

a person executing the double side kick technique,

recorded from a side view. Within the system, users

have the capability to upload both the video itself

and accompanying metadata regarding the performer.

This metadata is crucial for analysis purposes and in-

cludes details such as the leg used by the performer

for the kick and the performer’s height in centimeters,

as depicted in the video.

Figure 2: Upload of video including user metadata.

3.4.1 Analysis Algorithm

The analysis algorithm is a ﬁve-step process: (1)

coordinates for keypoints of the performer’s body are

detected and extracted from every frame of the video,

(2) the time series created by the set of coordinates

are cleaned by running them through an anomaly

detection, (3) 3D angles are calculated from the

extracted keypoints, (4) the calculated angles are

processed through an anomaly detection, (5) the

cleaned coordinates and angles are used to generate

the phases and nodes, as well as to extract and

calculate the performance indicators.

Image Processing: The frames of the video are pro-

cessed using the Openpose system (Cao et al., 2017).

Openpose takes a single frame as input, detects the

performer in the video and outputs coordinates for a

set of keypoints. The pose detection model is trained

on the COCO data set (Lin et al., 2014), which con-

tains 250.000 images of people each labeled with 18

keypoints of the human body. The coordinates of the

left and right shoulder, the left and right hip, the left

and right knee, the left and right ankle and the nose

of the performer are used in the following procedures.

Anomaly Detection: The anomaly detection com-

prises a set of predeﬁned rules that marks output of

the previous step as anomalies that are then not con-

sidered for the calculation. It uses a time series as

input, created by either the x-coordinates (horizontal)

or y-coordinates (vertical) of the kicking leg’s ankle,

knee or hip. In this context the coordinates refer to the

position in the 2D image currently being processed.

Two different type of errors occur in the output: (i)

The body part cannot be detected in the frame and the

coordinates are either missing or zero, (ii) the body

part position is falsely detected and the coordinates

are present but incorrect.

The ﬁrst type of error is handled by using the av-

erage between the ﬁrst preceding non-zero coordinate

and the next non-zero coordinate to interpolate the

missing value. The second type of error is handled

by the rule set. A rule takes a consecutive sequence

of coordinates as an input and returns a boolean

indicating that sequence represent an anomaly or

not. The rules check for differences in value between

successive points in the sequence or check the

monotony of the sequence.

Calculation Characteristics: The determination of

knee angles involves an initial estimation of the miss-

ing depth coordinates, which are not available in the

2D analysis as compared to the 3D analysis. To ob-

tain these coordinates, the distances between the knee

and hip, as well as the knee and ankle of the kicking

leg, are computed. These distances are then sorted in

ascending order, and the upper value corresponding to

the 95

percentile of each set is selected as the "real"

length of the knee and hip or knee and ankle. This es-

timation of the depth coordinate is calculated for each

frame of the video analysis.

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

132

The obtained value is utilized to construct a right

triangle, considering the detected positions of the

knee and ankle, for instance. This enables the esti-

mation of the depth coordinate. By doing so, a 3D

vector is generated, representing the line connecting

the knee to the ankle, as well as the line connecting

the knee to the hip. Subsequently, the angles between

these 3D vectors are calculated. Furthermore, the cal-

culated angle values undergo anomaly detection, em-

ploying a speciﬁc set of rules designed for this pur-

pose. The identiﬁed coordinates and calculated an-

gles are employed to detect the nodes that signify

the commencement and conclusion of distinct phases

within the technique. To determine the INI node, the

y-coordinates of the ankle of the kicking leg are ex-

amined, speciﬁcally by identifying the ﬁrst monoton-

ically increasing sequence of a predetermined length.

Regarding the MT1 node, the previously described

simple deﬁnition is utilized, involving the highest y-

coordinate of the knee prior to the angle surpassing

110 degrees. For the KE1 node, the ﬁrst monotoni-

cally decreasing sequence of angles subsequent to the

MT1 node is considered. The same respective rules

are applied for the detection of the MT2 and KE2

nodes.

To compute the velocity and distance characteris-

tics, the distance between the nose and ankle is uti-

lized. Similarly, the upper value of the 95

percentile

is employed to determine the true height of the per-

former, as provided by the user, and map it to the cor-

responding coordinates in the video. This allows for

accurate estimation and calculation of the performer’s

velocity and distance metrics.

3.5 Statistical Evaluation

Only 26 of the full 44 videos from the preliminary

study, were suitable (due to quality issues) for analy-

sis.

The differences between 3D and 2D measure-

ments were all prechecked for normal distribution by

Shapiro-Wilk test using a signiﬁcance level of α =

0.05.

The Bland-Altman plots estimated the average

mean difference agreement between the 3D and 2D

analysis for the phase duration in CH1 (-0.04 s; LOA

-0.15 to 0.06), KI1 (0.04 s; LOA -0.05 to 0.12), CH2

(-0.04 s; LOA -0.16 to 0.08), and KI2 (-0.01 s; LOA

-0.13 to 0.12).

Agreement for the femur angles at the node MT1

(-0.59

◦

; LOA -20.20 to 19.03), KE1 (4.92

◦

; LOA -

23.47 to 33.31), MT2 (4.13

◦

; LOA -13.64 to 21.89),

KE2 (-11.59

◦

; LOA -43.94 to 20.77).

Agreement for the leg spreading angles at the node

MT1 (4.25

◦

; LOA -14.47 to 22.97), KE1 (6.84

◦

; LOA

-17.63 to 31.31), MT2 (-0.36

◦

; LOA -16.04 to 15.33),

KE2 (-7.08

◦

; LOA -40.09 to 25.92).

Agreement for the relative knee height at KHK1 (-

5.78; LOA -29.76 to 18.20), KI1 (-3.30; LOA -23.95

to 17.36), KHK2 (-2.04; LOA -32.06 to 27.99).

Agreement for the relative shoulder knee distance

DSK 1(0.48; LOA -11.75 to 12.71), DSK 2 (1.41;

LOA -21.46 to 24.27).

A positive value of the average mean difference

indicates that the 2D analysis measured larger values

for this characteristic, and a negative value indicates

the 3D analysis has measured larger values compared

to the 2D analysis. The limits of agreement repre-

sent the range within which approximately 95 % of

the differences between the 2D and 3D measurements

will fall.

4 DISCUSSION

Generally it can be stated, that the main functional-

ity of the application is suitable for the purpose and

includes all deﬁned functional requirements. Fur-

thermore, all non-functional requirements were met.

However it must be noted, that not all videos could be

analyzed due to quality issues of the recording, imply-

ing that the uploads must have a certain standard for

further processing. Despite, these quality issues of the

source material, which led to exclusion of 18 videos,

the accuracy of the extracted data is rather high.

Furthermore based on the plots, the 2D measure-

ments do not have any signiﬁcant systematic bias for

any of the characteristics compared to the 3D mea-

surements.

In particular, based on the results of the Bland-

Altman plots, the hypothesis can be accepted for per-

formance indicators (i) Leg spreading vector at node

MT2, (ii) phase duration of CH1 and CH2, (iii) rel-

ative should distance DSK1 and relative knee height

KI1 but must be declined for the remaining distance,

angle, duration performance indicators.

There are studies that report a higher accuracy

in the kinematic analysis of 2D video (Schurr et al.,

2017; Peebles et al., 2021). In those two studies how-

ever the performer in the video remains in the same

position of the screen throughout the analysis. They

are either performing a single-leg squat or running on

a treadmill. The main challenge of the analysis in this

study was the movement of the performer, particularly

because of the missing depth parameters. There has

been a study using the Kinect

™

(Microsoft Corpora-

tion, Redmond, Washington, United States of Amer-

ica) 2D sensor for kinematic analysis (Pﬁster et al.,

2Trax3: Raising Accessibility and Everyday Use of Automatic Motion Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation

Algorithms

133

2014), but the results indicate that the measurements

cannot reach the accuracy of a 3D motion capture sys-

tem.

This study used only one type of camera and one

viewing angle for the recordings, which is common in

comparable 2D and 3D video investigations (Schurr

et al., 2017; Peebles et al., 2021; Remedios and Fis-

cher, 2021), although it would be interesting to exam-

ine the effect of different recording setups and dif-

ferent recording equipment on the analysis quality.

Particularly because it would better resemble the real

world environment, as athletes using the automated

analysis in their day to day training will not always

have the same recording quality and setup.

Besides effort and operation simplity, the cost fac-

tor is essential for integration of video analysis in reg-

ular training. The analysis in this study relies on hard-

ware that has a graphics processing unit. This type

of hardware is generally more expensive(Microsoft,

2023b). Similar analysis can be performed on cheaper

hardware that only contains a central processing unit,

however this leads to a much longer analysis duration

(Liang et al., 2019), which would prevent providing

instant feedback to the athletes.

Largely the challenges faced in this study are

similar to other studies with same pose estimation

software setup (Remedios and Fischer, 2021), which

means 2D video pose estimation is not yet ready to

replace more complex and expensive capturing sys-

tems, but serves well for performance estimations in

everyday training.

4.1 Limitations

The videos used for validating the application, were

recorded in 24 frames per second. A higher frame

rate would allow for a higher accuracy in measuring

the characteristics. In addition, the recording setup

for all of the videos was similar. Only a subset of the

test data was able to be used for validation, due to

poor video quality of parts of the test data (recording

equipment from approx. 2005). The use of differ-

ent camera types, video quality and recording setups

should be explored in a subsequent study.

5 CONCLUSIONS

Based on the current ﬁndings, it can be inferred that

the system, although in a prototypic stage, demon-

strates its suitability for automated analysis of 2D

double side kick videos. Moreover, it offers a conve-

nient and cost-effective means of technique analysis,

catering to a wide range of users, helping to identify

weaknesses for targeted innovative training (Hoel-

bling et al., 2021a) (Hoelbling et al., 2020). How-

ever, it should be noted that the system’s effectiveness

and accuracy heavily rely on scientiﬁcally established

performance indicators, as well as the quality of the

source material. In light of these considerations, it

can be concluded that the system serves as a valuable

tool for various applications.

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2Trax3: Raising Accessibility and Everyday Use of Automatic Motion Analysis in (Combat) Sports via ML Enhanced 2D to 3D Estimation

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