Reaction Time Estimation Based on Recursive Short-Term Principal

Component Analysis for Skeletal Information of Badminton Players

Kana Sagawa

, Hidehiko Shishido

, Masashi Suita

and Itaru Kitahara

Master’s Program in Intelligent Mechanical Interaction Systems, University of Tsukuba, Ibaraki, Japan

Center for Computational Sciences, University of Tsukuba, Ibaraki, Japan

Faculty of Health and Sport Sciences, University of Tsukuba, Ibaraki, Japan

Keywords: Badminton, Reaction Interval Estimation, Time-Series 3D Skeletal Information, Recursive Short-Term,

Principal Component Analysis.

Abstract: The aim of this paper is to measure the shot-reaction intervals of badminton players based on time-series 3D

skeletal information. In competitions where game dominance changes, effective plays and tactics in situations

can be investigated by analyzing the measured reaction intervals. In our proposed method, we estimated shot-

reaction intervals using a badminton player’s motion information and applied a short-term principal compo-

nent analysis to the sequential 3D skeletal information of athletes to extract features useful for motion analysis.

Hit and reaction times were detected by identifying the extrema in the first and second principal component

scores. We estimated a shot’s reaction interval from the hit time to the reaction time at which the player starts

moving in response. We applied the proposed method to the 3D skeletal information of a badminton player

and confirmed that reaction intervals can be estimated. By using the results of this study to provide feedback

to badminton players on the analysis of reaction intervals, players can learn and improve their effective and

ineffective tactics and plays.

1 INTRODUCTION

Data analysis is becoming more important in compet-

itive sports (Bhatnagar & Babbar, 2019). Both perfor-

mance and tactical analysis have investigated effec-

tive plays and the tactics of situations where game

dominance changes, such as soccer (Rein & Mem-

mert, 2019; Mackenzie & Christopher, 2012). For the

purposes of this paper, we use the term “game domi-

nance” as a degree of team dominance that changes

from moment to moment in a game. For such strategic

games as go and shogi, TV programs that stream AI-

based game analysis have emerged since grasping

game situations are difficult for average viewers.

Thus, interests in information provision and data

analysis techniques to help diverse users to better un-

derstand the game situation have been rising.

Competitive badminton is characterized by the

fastest shuttle speed (ball speed for other sports)

(Bańkosz et al. 2013). In a rally, a shuttle is returned

in approximately one second (Cabello & González,

https://orcid.org/0000-0001-8575-0617

https://orcid.org/0000-0002-5186-789X

2003), requiring that badminton players have the abil-

ity to move and react quickly. Reaction speed, return

position, footwork position, and connection to the

next move significantly impact performance (Kuo et

al. 2020). For example, if the reaction speed is slow

because the opponent's strokes are deceptive, the

game will be at a disadvantage. Therefore, knowing

the length of the reaction interval makes it possible to

know which plays affected the match. Furthermore,

the interval between the changes in a game situation

is very short, less than a second. Therefore, based on

the game situations in go and shogi, the application of

AI decision-making to badminton is impractical be-

cause such situations can change within one second

for each shot. On the other hand, we hypothesize that

information about effective plays and tactics may be

concentrated at specific times when game dominance

changes because these crucial points are related to

shot-reaction intervals. Therefore, the purpose of this

research is to measure the shot-reaction intervals of

badminton players and statistically analyze the inter-

vals to identify effective plays and tactics at the timing

Sagawa, K., Shishido, H., Suita, M. and Kitahara, I.

Reaction Time Estimation Based on Recursive Short-Term Principal Component Analysis for Skeletal Information of Badminton Players.

DOI: 10.5220/0012156900003587

In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2023), pages 15-22

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

Figure 1: Reaction interval estimation using skeletal information of a badminton player obtained by motion capture: Reaction

intervals are estimated as time from opponent's hit time to reaction time where player starts moving in response. By analyzing

reaction interval data, feedback is provided to coaches and players for estimating game dominance.

of changes in game dominance. We believe that the

visualization of such information can provide feed-

back to players and augment their performance.

As shown in Fig. 1, this paper estimates the reac-

tion intervals and reaction movements of badminton

players. Faster reaction speed is a key characteristic

in the reaction intervals of badminton players (Ca-

bello Manrique & González-Badillo, 2003), a theory

that has been confirmed with experiments using force

plates (De et al. 2023). However, it is unrealistic to

measure footwork movements during badminton

competitions with a force plate, because the size of

the equipment limits the movements that can be

measured. Therefore, in our previous work, we ana-

lyzed reaction motions using deep learning, which es-

timated the 3D skeletal poses of players from video

to determine their reaction intervals during badmin-

ton games. We used the acceleration information of

their waists to estimate a shot’s reaction interval.

Three types of movements occur during a game:

stroke movement, reaction movement, and transla-

tion. Separating the reaction movements and the

translation features was difficult using a classification

method based on waist acceleration. In addition, we

confirmed that the difference in the angle of the view

of the camera used in the training dataset and the tar-

get images distorted the estimated human poses. We

also confirmed that the estimation error lowered the

waist acceleration.

As shown in Fig. 1, this paper estimates the reac-

tion intervals and reaction movements of badminton

players utilizing a motion-capture system that ac-

quires more accurate skeletal position information.

The following are the main contributions of this pa-

per:

 a method that automatically measures the reaction

intervals of badminton players during games;

 a motion-extraction technique tailored for compe-

titions with different time intervals of motion,

such as badminton games;

 a method that identifies hit and reaction motions

by detecting the hit and reaction times.

2 RELATED WORKS

Deep learning has been used extensively in the field

of sports and biomechanics (Halilaj et al. 2018). It has

made skeletal pose estimation from images both ro-

bust and reliable (Badiola & Mendez, 2021). Open-

Pose (Cao et al. 2021), which is a representative

method for acquiring skeletal pose information by ap-

plying deep learning, can estimate 25 points of a per-

son's skeleton from input video in real-time. HRNet

(Sun et al. 2019), which maintains high-resolution

representation by connecting high-resolution and

low-resolution subnetworks in parallel, addresses the

problems of OpenPose, including frequent false pos-

itives when occlusion occurs and the low detection

rate of small objects. In both Cao's and Sun's studies,

the output is 2D skeletal coordinates on the input

video.

This research field has been extended by a study

in which a person’s 3D skeletal coordinates are output

from input video (Liu et al. 2021). Although estima-

tion methods for 2D and 3D skeletal positions using

deep learning have made remarkable progress, a

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

problem remains: the estimation accuracy drops sig-

nificantly during self-occlusion, which frequently

happens when the arms and legs are hidden by the

subject's body. Therefore, 3D motion-capture sys-

tems are the most commonly used method of acquir-

ing 3D skeletal information in the field of sports bio-

mechanics research (Zhao & Li, 2019).

In this paper, we propose a method that measures

the reaction intervals of players based on highly ac-

curate skeletal information obtained from a motion-

capture measurement taken during games. In the field

of motion analysis using time-series skeletal data, an-

other research (Xu et al. 2010) detected walking and

running rhythms by applying short-term principal

component analysis (ST-PCA) to motion-capture

data. In addition, another work (Federolf et al. 2014)

detected the motion characteristics of skiers by apply-

ing principal component analysis to motion-capture

data. When the entire body rotates while the foot mo-

tion represents the ground timing, we clarified that the

features representing whole-body motion can be ex-

tracted from the first principal component and fea-

tures representing partial motion can be extracted

from the second principal component. In badminton

games as well, whole-body translation occurs when

players hit a shuttle and such partial body movements

as extension and flexion of the hip and knee joints,

abduction and adduction of the hip joint and jumping

occur during footwork. We extract features that rep-

resent the whole-body translation during a hit using

the first principal component and features that repre-

sent partial body movements using the second princi-

pal component and estimate the reaction intervals

based on these features.

3 REACTION INTERVAL

ESTIMATION METHOD USING

RECURSIVE SHORT-TERM

PRINCIPAL COMPONENT

ANALYSIS

A reaction interval is estimated by observing the

player’s movements comprised of whole-body trans-

lation and partial movements. A whole-body transla-

tion is a movement with which a player advances to-

ward a hit point; partial movements include jumping

and swinging while moving toward a hit point. In sec-

tion 3.1 we explain how and why we define a reaction

interval as the pause between two types of keyframes:

hit times and reaction times. In section 3.2 we apply

ST-PCA to our time-series data. In section 3.3 we em-

ploy a preliminary experiment to demonstrate how to

use ST-PCA to detect both types of keyframes and

determine the reaction intervals.

We used an optical motion-capture system to get

the skeletal information of the players. 20 motion-

capture cameras were installed at 8 m to surround the

court. Skeletal information was obtained by a motion-

capture system, comprised of an OptiTrack Prime 41,

which captured images at 120 fps. “Motive: Body”

software was used as the motion capture system. We

attached 37 reflective markers to specified points on

the body of each player. The positions of 37 reflective

markers follow baseline (37) of Entertainment Mark-

ersets. A skeleton model of 19 joint points was

tracked based on the marker positions. Fig. 2 shows

the skeletal information obtained by the motion-cap-

ture system.

Due to the limitation of its temporal resolution,

the skeletal model lacks data during high-speed arm

swings. In such cases, linear interpolation compen-

sated for the missing data.

Figure 2: Skeletal information acquired by motion-capture

system.

3.1 Definition of Reaction Intervals

In badminton, when moving around the court, the fol-

lowing actions are repeated to respond to an oppo-

nent's shot: 1) pushing off from the playing center, 2)

decelerating toward the hitting point, and 3) pushing

off toward the playing center after stroke. Therefore,

in this research, the time of the opponent's hit is de-

fined as the beginning of the reaction interval, and the

reaction time (i.e., the time of pushing off at the play-

ing center to respond to the opponent's shot) is de-

fined as its end (Fig. 3).

Reaction Time Estimation Based on Recursive Short-Term Principal Component Analysis for Skeletal Information of Badminton Players

Figure 3: Definition of reaction interval: Time of opponent's

hit is defined as beginning of reaction interval, and reaction

time (i.e., time of pushing off at playing center to respond

to opponent's shot) is defined as end of reaction interval.

3.2 Short-Term Principal Component

Analysis

Short-term principal component analysis (ST-PCA)

is applied to the movements of joints (time-series

skeletal data) acquired by motion capture (Fig. 4).

The skeletal information is 𝐽3 𝑇 dimensional

data, where 𝐽 is the number of skeletal joints and 𝑇 is

the total number of frames included in the analysis

section. The skeletal coordinates are converted to

one-dimensional data for each frame to create 3𝐽𝑇

dimensional data (middle, Fig. 4). The skeletal data

for 𝑘-th frame 𝒑



are expressed as

𝒑



𝒙



, 𝒚



, 𝒛



,…,𝒙



, 𝒚



, 𝒛









𝑘1, … , 𝑇



(1)

To apply ST-PCA to the skeletal information, we

divided it into small analysis windows along the time

dimension. Similar to a previous work (Xu et al.

2010), the width of the analysis window is set to N

and the sliding width is set to the same value as the

analysis window width (lower part, Fig. 4). The di-

vided 3𝐽𝑁 dimensional time-series coordinate

vectors are analyzed in each analysis window. For ex-

ample, when applying ST-PCA to the 3D skeletal in-

formation of a badminton player, the 𝑇 frames from

the service to the end of the rally are used as the anal-

ysis section, and each bit of skeletal information is di-

vided into 𝑁 frames. Time-series coordinate vector

𝐏



in the 𝑖-th analysis window can be expressed as

𝑷



𝒑









,… ,𝒑







𝑖1,…,









(2)

In the last analysis window where the total num-

ber of frames in the analysis segment is not divisible

by 𝑁, 𝑷



is expressed by

𝑷











𝒑











,… ,𝒑



.

(3)

Figure 4: Application of short-term principal component

analysis to skeletal information: Skeletal information is

converted into 1D data frame by frame, and PCA is applied

to skeletal information for all N frames.

In ST-PCA, we applied ordinary principal com-

ponent analysis (PCA) to the standardized coordinate

vector 𝑷





of each analysis window. 𝑷





is calculated

by 𝑷





𝑷



𝑷



, where 𝑷



is the average posture of

the 𝑖-th analysis window. In PCA, the eigenvectors of

each principal component (PC) are calculated by the

singular value decomposition of the covariance ma-

trix of the input matrix. Each PC score is calculated

by projecting skeletal coordinate vector 𝑷





onto the

partial space on which each eigenvector is based. By

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

independently applying PCA to each analysis win-

dow, a discontinuity is created in the time-series of

the PC scores between two neighboring analysis win-

dows. A previous work (Xu et al. 2010) showed that

the coordinates can be smoothly combined by apply-

ing inversion and translation since the bases of the ad-

jacent analysis windows are temporally consistent.

Examples of the first PC scores before and after merg-

ing are shown in Fig. 5. Its top and bottom rows re-

spectively show the PC scores before and after merg-

ing. The time-series data, which have discontinuity

before merging, can be converted to continuous data.

According to a previous work (Federolf, 2016), the

PC scores represent the amplitude of the posture

changes in each principal component space.

Figure 5: (Top) First principal component scores before

merging: (Bottom) First principal component scores after

merging: Data that were discontinuous before merging are

converted to continuous data by inversion and translation.

ST-PCA is applied with 𝑁= 120, and

dashed

lines repre-

sent analysis window width.

3.3 Reaction Interval Estimation by

Recursive Short-Term Principal

Component Analysis

As described in section 3.1, the reaction interval of a

player is defined as the interval between the oppo-

nent’s hit time and the player’s corresponding reac-

tion time. The procedures to determine the hit and re-

action times are summarized below: 1) Perform ST-

PCA for Player 1 and find frame 𝑓



where the extreme

value of the PC1 score appears. 2) Perform ST-PCA

for Player 1 again but on an analysis window around

𝑓



. 3) From the result of step 2, find frame 𝑓



where

the extreme value of the PC2 score appears and label

it the hit time of Player 1. 4) Perform ST-PCA for

Player 2 on an analysis window around 𝑓



. 5) From

the result of step 4, find the frame where the extreme

value of the PC2 score appears and label it the reac-

tion time of Player 2.

We conducted a preliminary experiment by apply-

ing ST-PCA to the skeletal information of badminton

players. In a badminton singles match, since the play-

ers hit the shuttle in turn about every second [5], 𝑁

120 was set as the width of the analysis window for

the data captured at 120 fps. The ST-PCA results of

the two players are shown in Figs. 6 and 7. The former

shows the PC1 and PC2 scores and the hit times of

Player 1, and the latter shows the PC2 scores and re-

action times of Player 2. The blue lines in Fig. 6 and

the orange lines in Fig. 7 respectively show the actual

hit and reaction times. They were judged visually by

an experienced badminton player. The top and middle

rows of Fig. 6 and the top row of Fig. 7 show the time

series of the PC scores obtained from the ST-PCA re-

sult of a fixed analysis window described in section

3.2. The bottom row of Fig. 6 shows the time series

of the recalculated PC2 score of Player 1, obtained by

conducting a PCA on the 120-frame window around

the time where the extrema of the PC1 scores appear.

The bottom row of Fig. 7 shows the time series of the

recalculated PC2 score of Player 2, obtained by con-

ducting another PCA on the 120-frame window

around the detected hit time of Player 1.

Since the PC1 of the skeletal data represents the

translational and rotational movements of the whole

body within the analysis window, extreme values

were observed during the player’s braking move-

ments. PC2 represents the player's postural changes,

and thus extreme values are observed during arm

swinging, footwork, and at starting/ending of jump-

ing.

The local extrema of the PC1 scores, calculated in

a fixed analysis window, were located around the real

hit time but with a rather significant error (top row,

Fig. 6). The local extreme times of the PC2 scores are

closer to the real hit times compared to those of the

PC1 (middle row, Fig. 6). The error between the local

extremum of the recalculated PC2 scores and the real

hit time is even smaller, indicating a higher estimation

accuracy (bottom row, Fig. 6). Therefore, the time of

the extremum found in the recalculated PC2 score is

defined as the player's hit time.

The PC2 score of Player 2, calculated in a fixed

window in the neighbourhood of the hit time of Player

1, has a local extremum around the real reaction time

but with a large error (top row, Fig. 7). Since the local

extreme values of the recalculated PC2 scores of

Player 2 appear very close to the real reaction time

(lower row, Fig. 7), it is defined as Player 2’s reaction

time.

Reaction Time Estimation Based on Recursive Short-Term Principal Component Analysis for Skeletal Information of Badminton Players

In the process of calculating the local extrema,

prominence 𝑝, which represents the degree of promi-

nence of each peak, were calculated to reduce the

false positives caused by small changes (Cox et al.

2020). For the hit time detection, local extreme values

are considered when 𝑝𝑘, and if multiple local ex-

trema are detected in the 𝑡ℎ s, the local extreme value

is adopted with the largest prominence 𝑝. For reaction

time detection, the local extreme value with the larg-

est prominence in the PC2 score recalculated in the

neighborhood of the hit time is adopted. Recursive

principal component analysis was performed with a

window of analysis from frame 𝑏 before the reference

time to frame 𝑎 after it.

Figure 6: First two principal component scores and hit time:

Yellow areas represent analysis window. Blue line repre-

sents actual hit time specified visually.

Figure 7: Second Principal Component Scores and Reaction

times: Yellow Areas Represent Analysis Window. Orange

Lines Represent Actual Reaction Time Tolerance Specified

Visually.

We applied the above algorithm to both players to

estimate their reaction intervals. For example, when

estimating the reaction interval of Player 2, we de-

tected the hit time of Player 1 and the reaction time of

Player 2 and output the interval between them as the

reaction interval. An example of the calculated reac-

tion interval is shown in Fig. 8.

Figure 8: Reaction interval estimation using recursive

short-term principal component analysis.

4 EXPERIMENTS

We verified the effectiveness of our proposed method

through empirical experiments. The data used in the

verification were single matches of 10 innings played

by one female and one male member of the University

of Tsukuba badminton club. A badminton court was

set up in a 25-m-wide, 15-m-deep, 8-m-high space.

The subjects wore motion-capture suits to which 37

reflective markers were attached, and 20 motion-cap-

ture cameras and one fixed RGB camera were in-

stalled at 8 m to surround the court. Skeletal infor-

mation was obtained by a motion-capture system,

comprised of an OptiTrack Prime 41, which captured

images at 120 fps. The fixed camera was a Sony FDR

AX-55, which captured 1440  720 pixel frames at

30 fps. The fixed camera shot the video from the rear

of the court with the net in front of it so that both play-

ers can be seen in the video. “Motive: Body” software

was used as the motion capture system.

Our proposed reaction interval estimation algo-

rithm assumes that the hit times are correctly de-

tected. Therefore, in our experimental demonstration

of the proposed method, after an experiment that ver-

ified the hit time’s detection accuracy, we verified the

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

accuracy of the reaction time detection for the reac-

tion times to the correctly detected hit times. In addi-

tion, to verify the recursive principal component anal-

ysis’s effectiveness in the proposed method, we eval-

uated the method using only PC scores in a fixed anal-

ysis window and the proposed method using recursive

principal component analysis.

In experiments that evaluated the accuracy of the

hit time detection, we compared the following three

results: 1) the visual annotation results of the hit

times, 2) the detection results using only PC scores in

a fixed analysis window, and 3) the detection results

using the proposed method. The detection using only

the PC scores in a fixed analysis window is a method

that searches for the extreme times of the PC1 scores

and uses the extreme times of the PC2 scores (calcu-

lated in a fixed analysis window) immediately before

them as hit times. The results are correct if the abso-

lute error is within a threshold value; otherwise, they

are erroneous. Since the stroke times of badminton

players are approximately 0.3 to 0.4 s, the absolute

error threshold is set to 0.3 s.

We evaluated the accuracy of the reaction time de-

tection for the correctly detected hit times by compar-

ing the following three results. We compared the re-

action time detection results using the proposed

method with the reaction time detection results using

only PC scores in a fixed analysis window and visu-

ally annotated the reaction time ranges by experi-

enced badminton players. The annotated tolerance

range was set from the time when the players pushed

off their feet to when they started moving toward the

next hit. For the specified tolerance range, the output

by the proposed method was labeled correct if it was

within the tolerance range and incorrect if it was out-

side of it. If a shot in the hit time is an error and the

opponent does not react, no reaction time was output.

In this experiment, 𝑁 = 120, 𝑝 = 100, and 𝑡ℎ = 0.5,

where 𝑡ℎ was set to 0.5, assuming that in badminton

competitions, two hits by one player are never made

within 0.5 s (Cabello & González, 2003).

Table 1: Results of hit time detection accuracy evaluation.

Method

Recall

(%)

Precision

(%)

F-score

(%)

Simple

ST-PCA

Player 1 77.8 51.2 61.8

Player 2 73.1 52.8 61.3

Total 75.4 52.0 61.5

Recursive

ST-PCA

(a=90, b=30)

Player 1 88.9 68.6 77.4

Player 2 80.8 60.0 68.9

Total 84.8 64.3 73.1

Table 2: Results of reaction time detection accuracy evalu-

ation.

Method Recall (%)

Simple

ST-PCA

Player 1 34.8

Player 2 42.9

Total 38.8

Recursive

ST-PCA

(a=30, b=90)

Player 1 38.1

Player 2 47.8

Total 43.0

5 RESULTS

Table 1 shows the results of the hit time detection’s

accuracy evaluation and Table 2 shows the results of

the reaction time detection accuracy evaluation. Ac-

curacy evaluation showed that recursive ST-PCA out-

performed the simple ST-PCA in all the hit time de-

tection accuracy measures: Recall, Precision, and F-

score. Recursive ST-PCA also outperformed simple

ST-PCA in the precision evaluation of the reaction in-

terval detection. This result seems to be due to the fact

that simple ST-PCA does not allow for an analysis

window for a specific movement, whereas recursive

ST-PCA allows for an analysis window for the time

the hit or reaction movement occurred. In the hit time

detection, the hit times were undetected when the

player hit while jumping and when serving. False pos-

itives were observed at the start and end of a jump and

at the reaction time in the case of hits while jumping.

In the reaction time detection, a detection was made

when the highest point of a hop was reached immedi-

ately before a reaction, resulting in many cases of un-

detected times. When receiving a service, there were

cases where the first step after a push-off was not de-

tected at the time of the push-off, although it was de-

tected when the first foot touched the ground, result-

ing in undetected results.

6 LIMITATIONS

The system proposed in this study has the following

limitations, which still make it difficult to implement

an automatic analysis solution for actual badminton

matches.

 Skeletal information is captured using motion

capture with reflective markers on the athlete;

 Skeletal information is analyzed offline after ac-

quisition.

Reaction Time Estimation Based on Recursive Short-Term Principal Component Analysis for Skeletal Information of Badminton Players

7 CONCLUSIONS

We proposed a method for estimating reaction inter-

vals using recursive short-term principal component

analysis for the 3D skeletal information of players in

badminton games. Our proposed method detected the

extreme times of the second principal component

score near the time of the opponent's first principal

component score as the hit time of a reaction interval.

The extreme times of the other player's second prin-

cipal component scores near the opponent's hit times

were detected as the end of the reaction interval, and

the shot-reaction intervals were estimated. The results

of the detection accuracy evaluation showed that re-

call was 84.8% for the hit time detection and 43.0%

for the reaction time detection. The effectiveness of

recursive short-term principal component analysis

was confirmed in both detection accuracy evalua-

tions. The next step is to examine the relationship be-

tween reaction time and game dominance, and to use

reaction time and other factors as inputs to predict and

visualize game dominance.

REFERENCES

Badiola-Bengoa, A., & Mendez-Zorrilla, A. (2021). A Sys-

tematic Review of the Application of Camera-Based

Human Pose Estimation in the Field of Sport and Phys-

ical Exercise. Sensors. 21(18), 5996. https://doi.org/

10.3390/s21185996

Bańkosz, Z., Nawara, H., & Ociepa, M. (2013). Assessment

of Simple Reaction Time in Badminton Play-

ers. TRENDS in Sport Sciences 2013, 31, 54–61.

Bhatnagar, R., & Babbar, M. (2019). A Systematic Review

of Sports Analytics.

Cabello Manrique, D., & González-Badillo, J. J. (2003).

Analysis of the characteristics of competitive badmin-

ton. British journal of sports medicine, 37(1), 62–66.

https://doi.org/10.1136/bjsm.37.1.62

Cao, Z., Hidalgo, G., Simon, T., Wei, S., & Sheikh,

Y.(2021). OpenPose: Realtime Multi-Person 2D Pose

Estimation Using Part Affinity Fields. IEEE Transac-

tions on Pattern Analysis and Machine Intelligence,

43(1), 172–186.

Cox, B., Van der Perre, L., Wielandt, S., Ottoy, G., & De

Strycker, L. (2020). High precision hybrid RF and ul-

trasonic chirp-based ranging for low-power IoT nodes.

EURASIP Journal on Wireless Communications and

Networking 2020, 1, 187.

De Waelle, S., Robertson, K., Deconinck, F. J. A., & Le-

noir, M. (2023). The Use of Contextual Information for

Anticipation of Badminton Shots in Different Expertise

Levels. Research quarterly for exercise and

sport, 94(1), 15–23. https://doi.org/10.1080/0270

1367.2021.1934378

Federolf P. A. (2016). A novel approach to study human

posture control: "Principal movements" obtained from

a principal component analysis of kinematic marker

data. Journal of biomechanics, 49(3), 364–370.

https://doi.org/10.1016/j.jbiomech.2015.12.030

Federolf, P., Reid, R., Gilgien, M., Haugen, P., & Smith, G.

(2014). The application of principal component analy-

sis to quantify technique in sports. Scandinavian jour-

nal of medicine & science in sports, 24(3), 491–499.

https://doi.org/10.1111/j.1600-0838.2012.01455.x

Halilaj, E., Rajagopal, A., Fiterau, M., Hicks, J. L., Hastie,

T. J., & Delp, S. L. (2018). Machine learning in human

movement biomechanics: Best practices, common pit-

falls, and new opportunities. Journal of biomechan-

ics, 81, 1–11. https://doi.org/10.1016/j.jbiomech.201

8.09.009

Kuo, K. P., Tsai, H. H., Lin, C. Y., & Wu, W. T. (2020).

Verification and Evaluation of a Visual Reaction Sys-

tem for Badminton Training. Sensors (Basel, Switzer-

land), 20(23), 6808. https://doi.org/10.3390/s20236808

Liu, J., Rojas, J., Li, Y., Liang Z., Guan, Y., Xi, N., & Zhu,

H. (2021). A Graph Attention Spatio-temporal Convo-

lutional Network for 3D Human Pose Estimation in

Video. In 2021 IEEE International Conference on Ro-

botics and Automation (ICRA), 3374–3380.

Mackenzie, R., & Christopher, C. (2012). Performance

Analysis in Football: A Critical Review and Implica-

tions for Future Research. Journal of Sports Sci-

ences, 31.

Rein, R., & Memmert, D. (2019). Big Data and Tactical

Analysis in Elite Soccer: Future Challenges and Oppor-

tunities for Sports Science. SpringerPlus, 5(1), 1410.

Sun, K., Xiao B., Liu, D., & Wang, J. (2019). Deep High-

Resolution Representation Learning for Human Pose

Estimation. 2019 IEEE/CVF Conference on Computer

Vision and Pattern Recognition (CVPR), 5686–5696.

Xu, J., Takagi, K., & Yoneyama, A. (2010). Beat Induction

from Motion Capture Data Using Short-Term Principal

Component Analysis. Information and Media Technol-

ogies, 5(4), 1287–1293.

Zhao, X., & Li, S. (2019). A Biomechanical Analysis of

Lower Limb Movement on the Backcourt Forehand

Clear Stroke among Badminton Players of Different

Levels. Applied Bionics and Biomechanics 2019, 1–8.

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