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Journal of Ubiquitous Systems & Pervasive Networks
Volume 13, No. 1 (2020) pp. 01-09
* Corresponding author. Tel.: +201094586906
E-mail: habiba1611146@miuegypt.edu.eg
© 2020 International Association for Sharing Knowledge and Sustainability.
DOI: 10.5383/JUSPN.13.01.001 1
Usability Study of a comprehensive table tennis AR-based training
system with the focus on players' strokes
Ayman Nabil1, Habiba Hegazy1, *, Mohamed Abdelsalam1, Moustafa Hussien1, Seif
Elmosalamy1, Yomna M.I. Hassan1, Ayman Atia2,3
1 Faculty of Computer Science, Misr International University, Egypt
2HCI-LAB, Faculty of Computers and Artificial Intelligence, Helwan University, Egypt
3October University for Modern Sciences and Arts (MSA), Egypt
Abstract
Table tennis game is based on the speed of the player’s response to different attacks and defense
strokes. A way to enhance the player’s performance and technique while training is to update the
player with the mistakes in real-time. This paper presents a system that focuses on detecting the
correct and wrong strokes within the following stroke types: Forehand drive, backhand drive, and
forehand topspin. By the usage of Augmented Reality, the system helps the players to get their
results and direction easily using AR-based mobile application when practicing real-time. A
usability study has been made to measure the learning style of the players by letting the players train
on different strokes with the system. Moreover, an experiment has been done to measure the
efficiency of the application and compare different algorithms to overview their performance in
identifying the strokes based on accuracy and time taken.
Keywords: Stroke Classification, Stroke Identification, Table Tennis, IR Depth Camera, Hand
Gestures, Augmented Reality
1. Introduction
With the presence of modern technologies like smart bands,
mobiles and, advanced glasses, technology become an
incredible aspect of our everyday. In sports particularly,
technology is utilized to help increase the player's performance
and playing technique through the training sessions. Sensors
were used to help in detecting the player's motion and strokes.
Nowadays, virtualization technology such as Virtual Reality,
and Augmented Reality headsets are integrated into the sports
field to notify players and improving their training
environment. In table tennis, technology was investigated in
the creation of the electronic scoreboard, automated scoring
system, interactive ping pong table, and high-speed camera's
and recently, technology was shown to create robots to
compete with. On the other hand, any technology that can be
created to points on the player's correct and wrong movements
to predict or measure the player's performance will be very
useful [1]. This makes the human-computer interaction in table
tennis an important research field and important to increase the
player performance and speed. According to statistics made in
the United States in 2017, there are 16.04 million players
around the world play table tennis [2]. This paper focuses on
three different types of strokes such as forehand topspin,
forehand drive and, backhand drive movements.
One of the basic table tennis strokes is a forehand drive. It is
a stroke of an attack with a little percentage from the topspin
stroke. It is performed as a reply for medium or long-range
topspin or ball drifts. According to Ben Larcombe [3] a
forehand drive should be done correctly on six steps: The
racket should be ranging between the waist height, close the
racket angle a little bit, lean backward from the waist by a
small amount, when the ball arrives, turn your racket and move
it forward towards the table and up, contact the ball instantly
when it comes next to your body, the touch would have been
very smooth, just in the center of the racket. These steps are the
same for either a forehand drive or backhand drive. On the
other hand, a common mistake is putting your arm across your
body on the follow-through this is going to slow your recovery
for the upcoming shot as shown in fig. 1 (a).
The backhand drive stroke is one of the most important
strokes in table tennis sport, according to Killertips Network,
L. Sharon, and W. Suwito [4] there are four sections for the
backhand drive; the backswing, the stance, the finish, and the
strike. Mainly the stance consists of positioning your feet,
knees, body, and arms to get started to the shot. The backswing
is about keeping your hand in the right place and placing the
racket in front of your belly. The strike is the part where the
player starts hitting the ball as if it's the topspin to create some
Author et. al. / Journal of Ubiquitous Systems & Pervasive Networks, 1 (2020) 01-09
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momentum. Lastly, the finish where the bat should be in front
of the player's chin. Players usually make some common
mistakes while playing a backhand drive such as playing the
stroke as a short shot and standing directly behind the ball
while it's coming as shown in fig. 1 (b).
The forehand topspin stroke is a complete attacking stroke
where it takes all the power from the player to get the ball to
the maximum speed possible to make one of two things, to
make it harder on the opposite player to block or to use it to
block an opponent spin. The technique used to make a correct
forehand topspin [3, 4]: Begin with your racket under waist
height, close your racket slightly, twist your knees, and lean
backward from your lower body. As the ball gets to you, push
your legs up, twist forwards, and accelerate your racket
upwards. One of the common mistakes for doing a topspin is
either close to the racket angle or open it just before ball
contact, which means that the racket angle is not correct, each
stroke is different. This leads to inefficient play with very bad
stability in the player's hands as shown in fig. 1 (c).
(a) The correct and wrong techniques while the player was performing
forehand drive stroke.
(b) The correct and wrong techniques while the player was performing
backhand drive stroke.
(c) The correct and wrong techniques while the player was performing
forehand topspin stroke.
Fig. 1. Clarification of table tennis strokes techniques during the
game.
This paper is an extension work for our previous papers [5,
6], the main contribution of this paper relies mainly on
classifying the different stroke types (correct/wrong) through
various algorithms. Also, to support the system with an AR
notification system to increase the table tennis player's skills to
the next level by giving the users real-time performance and
results. Also, we aim to measure the learning style of the
players by undertaking a usability study.
The paper is partitioned into four sections. Firstly, the
related work where we present other researches in different
domains related to the techniques used in the proposed system.
Secondly, the methodology where we mainly explain the
proposed approach and the techniques used. Then we go to the
experiment where we show the proposed system contribution
and results. Fourthly, a discussion where we investigate the
basis of the introduced platform. Finally, summary and future
studies where the results and outcomes of the research are
presented.
2. Related Work
This section is split up into various parts. Each part is
required to indicate how it is used in our framework.
2.1. Systems of table tennis
In this sport, movements are categorized into either primary
and advanced stages. Drive, topspin, and push are crucial basic
strokes. It can determine and identify tennis strokes by
capturing the motion of the hand. The research [7] was used to
assign a hardware component to the racket to track player
movements. Some studies achieved 95.7% accuracy [8] by the
use of this methodology. By thus the existence of wearable
devices such as wearable IMU systems and utilizing basic
equations [9] reached a significant classification result for table
tennis serves, forehand, and backhand strokes.
The classification of ball spins was mentioned in another
research. Many kinds of strokes lead to many spins and speeds
of the ball. [10] offered a ball speed and spin system, inserted
within the table racket, with a single IMU, researchers reached
a precise of 79.4%. In a different study, a robot has been made
by the researchers that can compete with a human player [11].
Therefore, detection and classification of the strokes in table
tennis are known to be a hand motions problem based on hand
movements as table tennis sport.
2.2. Hand gestures
In human-computer interaction, the recognition of hand
motions is considered to be a genuine domain. It provides
multiple techniques that are considered close to the behavior of
humans [12]. Gestures act as a very significant and efficient
technique, especially through sport training applications,
Augmented Reality, and online environments. Using Kinect
and SVM algorithm, [13] offers a suitable solution to weighty
HCI interfaces in real-time which results in 95.42% accuracy.
Authors in the [14] developed a multi-core DTW solution,
0.28-second time-consuming, multi-core algorithm. Hand
movements to be observed will be recorded. In detecting
movements and actions of the body the sensors play a
significant function [15, 16]. IR Depth camera is one of these
sensors.
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2.3. Background of the IR depth camera sensor
The idea of smart technology involves the use of knowledge
in various technologies and areas. Sports are one of these areas.
Consequently, people seek to efficiently utilize technologies to
increase sport competitive environment. The identification and
recognition of the player's movements are the primary goals of
the development aspect of the sport. Detection of movements
achieved generally using a camera or wearable sensor
movement devices in a specific IR depth camera. The use of IR
depth cameras like Kinect which will be a vital role in the
sports field in the future such as folk dance [17]. In [18]
authors used the SVM algorithm and IR depth camera to
identify the human posture to reach optimal accuracy for
various positions. The results indicate that Kinect can identify
various positions in high accuracy with a low-cost device. In
addition, [19] research has pointed out how Kinect SDK can
identify joints correctly and it has proposed an AR judo
training program. The use of IR depth cameras including
Kinect also plays a leading part in the development of the
sports sector.
Neither of the previous studies stated that an IR depth
camera had been used in table tennis, relying on player motions
for the optimal performance. The paper has a maximum
exactness of 96.29% [20] instead of an IR-depth camera used
for the performance of the player when balls are received. A
large amount of noise is often produced by the usage of the IR
depth camera which affects data collection and consequently
the system classification [21]. Therefore, the filtering phase
with sensors is an essential prerequisite.
2.4. Pre-processing (Filters)
Tools like IR-depth cameras and other sensors identify data
with a huge background noise [21]. Although there's a real
need to remove noise, due to the light systems, and the data is
encircled by noise. The bilateral filter for the joint was used for
the identification of captured images in order to improve image
quality [22]. [23] used the Gabor Filter with the SVM
algorithm to resolve a hand motion detection procedure which
removed lighting limits. In addition, the Gabor filter improved
the accuracy of the recognition. Moreover, Nuttaitanakul and
Leauhatong [24] utilized the channel length for human action
characterization filtration. On the other hand, the Kalman filter
has been used to monitor the body joints to remove the noise of
the unwanted vibrations and thus decreases the variation in the
center of joint position [25]. Furthermore, the Kalman filter
contributed by dismissing signals to create a moving object
position and orientation in a real-time application using sensor
motion devices which affect signal reliability [26]. The main
aspect left is to identify the movements after recording the
gestures of table tennis utilizing the IR-depth camera. One of
the most significant classifiers is the dynamic time warping
algorithm for real-time systems.
2.5. Hand Gesture Algorithms
By the usage of the signal taken from the IR depth camera,
we need to classify the movements and strokes made by the
player. Numerous and various algorithms were utilized in
various hand signals application. The study [27] presented an
android application online for evaluating the strokes performed
mounting the smartphone to the player’s wrist using the
decision tree algorithm, reaching to 77.21% and 69.63% on
average accuracy. According to Chao Xu et al., [28] they tested
the different algorithm on Finger-writing with Smartwatch
system, reaching a result that out of naïve bayes, logistic
regression, and decision trees, logistic regression was the best
classifier with 99.20% and 97.10% for detecting finger and
hand movements sequentially. Also, the use of the Random
Forest algorithm was used for automatic hand motion
recognition where authors reached an accuracy of 98% [29].
The usage of a convolutional neural network (CNN) algorithm
was mainly used in the sport in the field of image processing.
[30] represented a real-time table tennis forecasting system, the
usage of CNN was to do the 3D pose estimation with the usage
of an RGB camera. Moreover, the study [31] suggests a quite
effective online classification and recognition method of null or
negative lateness in hand movements by the usage of CNN.
In addition, Dynamic Time Warping (DTW) examines the
appropriate resemblance among two-time series with particular
limitations. Any information can be analyzed using DTW,
which can be converted into a linear sequence. They utilized
dynamic time warping and IR-depth camera in activity
recognition for human development because of their solidity of
pace or design while executing the tasks [32, 33]. In another
instance, the usage of a smart band that consists of an
accelerometer and gyroscope with the DTW algorithm [34].
The authors of [35] presented a system to train tennis shooting
data on two levels of hierarchical classification based on the
principle of using the DTW and QDTW. [36] Authors used
Microsoft Kinect to develop the recognition of hand
movements using DTW and HMM algorithms. Researchers
found that at the moment of the consideration of a requirement,
DTW was a better option than HMM.
Due to the FastDTW appearance, the speed and accuracy of
systems had been different [37]. This has assisted other
applications to reach 98% accuracy in real-time. Authors in
[38] introduce a methodology that optimizes the concept of
athlete coaching through the use of an IR camera to recognize
the athlete’s wrong joints and notifies him/her prior to the
occurrence of the injury. Their outcomes indicate that the
approach of FastDTW has surpassed different methods and
may achieve a 98% accurate recognition of dependent user
gestures. The classified data would be used to get information
to the user through an interface, one of those interfaces is
augmented reality.
2.6. The use of Augmented Reality in hand gestures
Augmented Reality can turn into the irreplaceable apparatus
which will be utilized by all players to get constant information
about each activity or movement they make. The usage of
Augmented Reality with self-directed sessions, sustained
guidance, and feedback in training or learning sessions can
improve the playing style and performance [39]. Authors in
[16] suggested an AR framework, primarily in sports, that
provides guidance and feedback. The major difficulties in the
area of hand gestures would be to have the best classification
accuracy, as the variety of other ways people perform gestures
with different speed levels.
Moreover, authors of [40] proposed an improved 3D ping
pong platform for two players through Wi-Fi access called
ARPP. Where the players can control the rackets by moving
their smartphones. In the volleyball sports field, the study was
made to evaluate and analyze the load of the knee for the
players in volleyball by jumping into Augmented Reality (AR)
during preserving the perceptive associations by repeating the
Author et. al. / Journal of Ubiquitous Systems & Pervasive Networks, 1 (2020) 01-09
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visual characteristics of the court of volleyball. The study
shows that AR can be used to better replicate competitive
information from a clinical assessment [41]. Besides, a
research study made to examine the boundaries and plan
prerequisites for creating a headset for downhill winter athletes
for Augmented Reality that can enhance spatial-awareness,
visual perception, and lessen injury [42]. However, AR was
also used for navigation in lots of systems. Where authors [43]
proposed a system based on a usability study for Indoor
Library Navigation, they used AR technology to quickly
enhance user functionality and knowledge inside the library in
the right direction.
3. Methodology
The system consists of several sections. Firstly, the
collection of data using an IR depth camera, in which data is
collected for different joints. Secondly, a pre-processing
section consisting of the segmentation of strokes and the
filtration of data. Thirdly, the feature extraction section for the
identification of critical data features. Fourthly, the processing
section where the strokes are classified. Finally, the interfaces
showing how data is displayed on both mobile devices and
augmented reality as shown in fig. 2.
Fig. 2. Block technical diagram and system architecture.
3.1. Data Acquisition
The acquisition of the data is performed by utilizing the IR
depth camera SDK, which differentiates different joints
(elbow, shoulder, wrist, and waist) as shown in fig. 3 from the
player's body. This approach of data gathering wasn’t affected
by environmental factors. The IR depth camera receives three-
dimensional points (X, Y, Z), for each joint. The obtained data
is transferred to the inner room server linked to the IR depth
camera.
3.2. Pre-processing
This phase involves filtering, segmenting, and modifying
data collected by the sensor just before the phase of processing.
3.2.1. Stroke Segmentation
After data acquisition, the stroke should be segmented from
the collected source of data. In table tennis strokes, the system
measures the Euclidean distance between the main starting
point and the following points to which the player is allocated.
The Euclidean distance is utilized in identifying and detecting
any stroke from the sensor sequence. The length of the section
which linked the two points is tested by the Euclidean distance.
Fig. 3. The framework tracks and records the player's joints and
its developments identified with table tennis strokes
3.2.2. Data Filtering
The Kalman filter is used for removing noise from the IR
depth camera. For noise reduction and movement tracking
Kalman filter preferred. It is used to improve the precision of
the IR depth measurement and shows an appropriate pre-
processing and precise change, as described in [25, 26]. Due to
the high-frequency variations in the joint location and skeleton
extraction, two forms of noise impact the IR camera depth time
series results. Our obtained data is passed through the Kalman
filter to enhance the IR-depth camera position estimation and
to remove the noise affecting data such as temperature change,
gravity, vibration, etc.
3.2.3. Feature Extraction
Extraction of the features is an important phase before the
classification of the data and provides more information about
the data and the mechanism. It gives us more understanding of
the data and increases the performance and accuracy of
learning algorithms [44]. Through this stage, the perception of
the gathered data and the initial raw data set was decreased for
the processing phase to more manageable and related
categories. Multiple feature extraction techniques are used to
decrease the number of resources available for evaluation
without lacking significant or specific information and classify
essential data characteristics. Multiple feature extraction
techniques were used such as mean, median, minimum, and
maximum.
Author et. al. / Journal of Ubiquitous Systems & Pervasive Networks, 1 (2020) 01-09
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3.3. Processing
After the pre-processing phase, the collected data is being
labeled using FastDTW. It is an analysis of time series and
algorithms used to calculate the connection between two
separate time and speed sequences. FastDTW provides near-
optimum alignments to DTW and O(N) complexity of time and
memory that requires O(N2).
FastDTW was developed on the basis of Chan and Salvador
[37], utilizing a multi-level approach with three main
operations. FastDTW is really useful. First, the coarsening of
the data points to the time series. Second, a prediction is
primarily found on a minimum distance warp trajectory in the
lowest part of the stroke flow. Finally, optimizing the
disruption path generated by local warp trajectory alterations
from the lower part of the stroke current. FastDTW decreases
the time series in DTW by this approach.
First, a cost matrix begins to be established between the
player's stroke and every stroke in the dataset. FastDTW begins
to calculate the initial value on a matrix between the stroke
points and each stroke point of a dataset with the two points
and the neighbor.
Second, The FastDTW uses backtracking of the greedy
search and the cost matrix to only reach the gap through the
two strokes. In addition, FastDTW typically begins to
distinguish among these two strokes by adding the element to a
cost matrix at the top left. The algorithm ensures that each
stroke in the data set reaches a balance of the distance from the
test stroke. Eventually, FastDTW starts to search for the
optimal distance of equilibrium. Thus, the label of the stroke
may be identified from the dataset.
Although, in order to test the efficiency of FastDTW
algorithm, we compared this algorithm will logistic regression,
random forest and decision tree which were implemented in
their default structure. Moreover, we compared the FastDTW
algorithm with the Convolutional neural network algorithm
which was implemented by the model presented in the research
presented by Devineau et al., [45]. The model is based on using
CNN classifying dimensional data and not depending on image
processing.
3.4. Augmented Reality Interface
Augmented Reality (AR) is a technique that deals primarily
with the graphics, sound, video, and many other sensor-based
data on physical objects, utilizing the camera interface and
using the computer-based vision recognition algorithms. By the
usage of AR, the system can give real-time feedback to the
player instead of the old technique used of recording the
training session and then entering the video recorded into
software that analyzes their sessions and get them a report of
how many wrong and correct strokes they have done.
The Augmented Reality was built to give certain
notifications for the player such as: if the stroke is wrong or
correct, and if the stroke was wrong the system notify the joint
where the mistake took place as shown in fig. 4. In the
experiment section, we will test the usage of Augmented
Reality and make a usability study on learning style with the
players which tried the system.
4. Experiments
In this part, we set out the environment needed, and we
collected the dataset. We present the algorithm comparison for
the stroke classification in our system. Also, we present an
experiment on usability study divided into two sections the
usage of AR and the learning style of the player.
Fig. 4. AR display screen to notify players while training
4.1. Equipment Setup
With the original IR depth camera in the proposed platform,
gesture recording was accomplished. The IR depth camera was
utilized to monitor X, Y, and Z directions and its timestamps of
four joints of the skeleton. This camera is placed on the table,
78 cm from the ground while the player is set to be 152 cm
away as shown in fig. 3.
4.2. Data Collection
Overall, around 1200 trials were collected on three main
strokes: forehand drive, backhand drive, and forehand topspin,
from five professional players (three males and two females).
Every player had to repeat 80 times every stroke with a total of
240 trails per player. Testing is equally divided into correct and
incorrect strokes.
4.3. Experiment (1) – Stroke Classification Accuracy
In this experiment, our system evaluated FastDTW, CNN,
Logistic Regression, Random Forest, and Decision Tree
algorithms for detecting and classifying the three main strokes
of table tennis performed correctly and incorrectly. We used
150 samples for training and 50 samples for testing for each
correct/wrong stroke with a total of 900 samples for training
and 300 for testing. We also calculated the time that it is taken
by each algorithm for classifying a single played stroke. The
results of the algorithms are shown in table 1.
4.4. Experiment (2) – Usability
The usability experiment was done with the help of 50
different participants in the age group of (13 – 28). The
percentage of females where 45% and males where 55%.
Moreover, 60% of the players where international table tennis
Author et. al. / Journal of Ubiquitous Systems & Pervasive Networks, 1 (2020) 01-09
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players and 40% where national table tennis players.
Participant's preferences were indicated as to the number of
questions they were asked. Firstly, we asked them to give us a
brief about them and their expertise in table tennis. Secondly,
they started rating the system from the weakest points to the
strongest, covering all the critical and major usages of the
system. Such as performance, complexity, setup,
destructibility, comfortably, and the whole experience with IR
depth camera and AR glasses.
4.4.1. Usage of Augmented Reality
Overall, users’ perceptions of the performance usability of
the system were very positive. The usage of AR was reported
as highly comfortable 40 of 50 of the players were very
satisfied as shown in fig. 5 (b). Moreover, as AR is a head-set
wearable device. we focused also on is the AR screen display is
distributive or not. We came out with a result that 36 out of 50
players agreed that it was not distributive as shown in fig. 5 (a).
Participants also indicated that they would not hesitate to wear
the AR head-set and that they did not experience any negative
reactions while wearing it. Moreover, their feedback on the
navigation on the AR that was very reasonable and satisfying
to them.
Fig. 5. Players' opinions for the usage of Augmented Reality in
the sports field
4.4.2. Learning Style
We constructed an experiment that shows how well the
system improves the playing style of the players. In this
experiment, we asked 20 beginners’ players to perform the
three different strokes of the table tennis (forehand topspin,
forehand drive, and backhand drive) in all of the three sessions.
Each session contains the average percentage of mistakes made
by players on all three strokes. Each player played the three
strokes around 25 times by focusing on improving the style
according to the system instructions. The average number of
mistakes is not the direct number of mistakes. The aim was to
measure the enhancement of the player at each stroke.
As shown in graph fig. 6 the number of mistakes produced
by the players decreased by each session. Moreover, we asked
the users to answer a survey question whether their
performance enhances and 90% concluded that the system did
enhance their learning curve.
Fig. 6. Usability study on player’s learning performance
5. Discussion
According to the results shown in table 1 illustrates how the
system was successful in the detection and classification of
strokes. FastDTW and CNN have shown the highest accuracy
for each class and overall average accuracy. Since our system
is time-sensitive to provide real-time feedback we depended on
FastDTW. This algorithm has ideal or near-optimal
synchronization, with O(N) time and memory complexity. It is
an estimated time and frequency algorithm. However, the
dependence of CNNs on the initial parameter tuning to avoid
local optimal, this mostly computationally expensive because it
has to take a large dataset for training. We also conducted a
confusion matrix on our chosen algorithm FastDTW ro clarify
more our results as shown in table 2. Which was built on the
same data used in table 1. Moreover, the player's mistakes
decreased during the usage of our system, and this is due to
feedback in real-time the system provides for the player of the
mistakes made in each stroke and overall performance of the
session.
5. Future work and conclusion
In brief, during this study, we have proposed a framework
to train table tennis players and increase their performance. The
Table 1. Different classification algorithm comparison
FastDTW
CNN
Logistic
Regression
Random forest
Decision Tree
Correct backhand drive
94%
96%
86%
78%
80%
Correct forehand drive
98%
98%
86%
82%
78%
Correct forehand topspin
94%
92%
84%
78%
76%
Wrong backhand drive
90%
92%
80%
80%
72%
Wrong forehand drive
96%
96%
86%
86%
78%
Wrong forehand topspin
92%
92%
82%
74%
70%
Average Accuracy
94%
94.33%
84%
77.33%
75.67%
Average time (single stroke)
0.98 sec
2.15 sec
1.73 sec
2.08 sec
0.99 sec
Author et. al. / Journal of Ubiquitous Systems & Pervasive Networks, 1 (2020) 01-09
7
system was built on the basics of strokes in table tennis.
Moreover, by the utilization of an IR depth camera, the system
acknowledges the mistakes made by the player in his/her
playing technique. In addition, the usage of AR was very useful
and was not either distributive or uncomfortable. Based on the
experiments tested using different classification algorithms and
sensors on various players, we have concluded the system was
able to increase the player’s performance by 66%. Our future
work targets adding extra advances to the framework
informational index, normalize, and get with an advanced level
in utilizing the AR.
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Table 2. Confusion matrix on FastDTW algorithm
CFD
CBD
CFTS
WFD
WBD
WFTS
PRECISON
CFD
47
0
0
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0
0
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CBD
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