IoT-Based Instrumentation Development for Reaction Time, Kick Impact Force, and Flexibility Index Measurement

Abstract

This paper presents an Internet of Thing (IoT)-based instrumentation development using vibration sensor, force sensor, and Blynk app to obtain the reaction time, kick impact force, and flexibility index based on visual stimulants. Besides the Blynk app, vibration, and force sensors, other main components of the prototype are the Arduino NodeMCU microcontroller, and three LEDs (yellow, red, and green). The developed prototype was able to record the reaction time, kick impact force, and flexibility index. These outputs could be viewed not only on the Organic Light Emitting Diode (OLED) display but also on the smartphone via the Blynk app interface. To evaluate the performance of the developed prototype, four male Silat athletes weighing between 61kg to 80kg were recruited for this experiment. They were requested to undergo a Simple Reaction Time (SRT) task which requires them to perform three trials of the front kick. From the SRT findings, it can be deduced that 75% of the participants reacted faster towards green LED with the fastest reaction time recorded was 1485.2±126.7ms compared to yellow and red LEDs. In the future, the design of the hardware in terms of circuitry and hardware casing will be improved for a better prototype presentation. Secondly, a big sample size of subjects and different branches of combat sports will be recruited to assist in further analysis. Furthermore, the sound stimuli will be included in future studies. Lastly, further research should be conducted to assess the effect of colored stimuli on the reaction time of the athletes.

Full text

IoT-Based Instrumentation Development for
Reaction Time, Kick Impact Force, and
Flexibility Index Measurement
Chun Keat Ng and Nur Anida Jumadi
Department of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Universiti Tun Hussein Onn
Malaysia, Batu Pahat, Johor, 86400, Malaysia
Email: chunkeat0116@gmail.com; anida@uthm.edu.my
Abstract—This paper presents an Internet of Thing (IoT)-
based instrumentation development using vibration sensor,
force sensor, and Blynk app to obtain the reaction time, kick
impact force, and flexibility index based on visual stimulants.
Besides the Blynk app, vibration, and force sensors, other
main components of the prototype are the Arduino
NodeMCU microcontroller, and three LEDs (yellow, red,
and green). The developed prototype was able to record the
reaction time, kick impact force, and flexibility index. These
outputs could be viewed not only on the Organic Light
Emitting Diode (OLED) display but also on the smartphone
via the Blynk app interface. To evaluate the performance of
the developed prototype, four male Silat athletes weighing
between 61kg to 80kg were recruited for this experiment.
They were requested to undergo a Simple Reaction Time
(SRT) task which requires them to perform three trials of
the front kick. From the SRT findings, it can be deduced
that 75% of the participants reacted faster towards green
LED with the fastest reaction time recorded was
1485.2±126.7ms compared to yellow and red LEDs. In the
future, the design of the hardware in terms of circuitry and
hardware casing will be improved for a better prototype
presentation. Secondly, a big sample size of subjects and
different branches of combat sports will be recruited to
assist in further analysis. Furthermore, the sound stimuli
will be included in future studies. Lastly, further research
should be conducted to assess the effect of colored stimuli on
the reaction time of the athletes.
Index Terms—Internet of Things, reaction time, kick impact
force, flexibility index
I. INTRODUCTION
There is no consensus on the exact definition of agility
within the world of sports science. One of the proposed
definitions of agility is a rapid reaction of whole-body
movement with a change of velocity or direction in
response to the stimulus. Agility comprises physical
qualities that can be trained, such as power, strength,
flexibility, and technique, as well as cognitive
Manuscript received May 21, 2021; revised August 15, 2021;
accepted September 20, 2021.
Corresponding author: Nur Anida Jumadi (email:
anida@uthm.edu.my).
This research was supported by Research Management Centre
(RMC), UTHM under GPPS Grant No. H314 and TIER 1 Grant No.
H226.
components, such as visual processing techniques, visual
processing speed, anticipation, and pattern recognition [1].
An athlete, who has an advantage on their physical and
cognitive skills, is said to give better performance, and
this performance is evaluated by the reaction time of the
athlete itself. Athletes who have better reaction time are
said to have an ideal performance than others [2]-[6].
Silat performers are required to make use of their eyes,
hands, and feet to execute defense and attack techniques.
At present, there are hardly any devices that measure the
reaction time, kick impact force, and flexibility altogether,
especially in Silat sport. Therefore, in this study, a
developed prototype that can display the reaction time,
kick impact force, and flexibility index of the Silat
practitioner will provide significant data for the coaches
to plan the training programs to achieve great
performance success. The flexibility of the athletes is
important as it can help to prevent sports injuries [7].
Based on the current limitation, the objectives of this
research are formulated. The first objective is to develop
an IoT-based instrumentation device for measuring the
reaction time, kick impact force, and flexibility index of
Silat athletes based on Simple Reaction Time (SRT) task.
The second objective is to evaluate the performance of
the developed device on Silat athletes by requesting them
to perform the front kick on the prototype. To achieve the
objectives of the research, the scopes of the research are
divided into three phases. The first phase is the
development of Internet of Thing (IoT)-based
instrumentation, which measures reaction time
(milliseconds), kick impact force (Newton), and
flexibility index (dimensionless). The main components
of the device consist of Arduino NodeMCU, vibration
sensor, force sensor, and three-light emitting-diodes
(LEDs). Besides, the circuit design is simulated by using
Proteus software for circuit operation testing. The second
phase is the integration of all parameters with the (IoT)
platform. In this study, the Blynk is chosen as the IoT
platform. Through the integration with the Blynk
platform, the data obtained can be viewed in the Blynk
app and it can be stored in the Blynk server for later
analysis. The final phase of this study is the data
collection on the participants. This study is carried out on
Silat athletes who were highly skilled and had
participated in the regional tournament. The experiment
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
©2022 Int. J. Elec. & Elecn. Eng. & Telcomm. 82
doi: 10.18178/ijeetc.11.1.82-87

was conducted in the outdoor environment for
participant's comfortability and according to the usual
training environment. The prototype was attached to the
kicking pad, and the participants were required to
perform the experiment task known as Simple Reaction
Time (SRT). The prototype was designed to be able to
withstand the impact of the kicking force so that any
major damages to the prototype can be avoided.
II. DEVELOPMENT OF IOT-BASED INSTRUMENTATION
Fig. 1 shows the overall block diagram of the proposed
IoT-based instrumentation. The Arduino NodeMCU is
used as the microcontroller to communicate with the
vibration sensor and the force sensor, as well as for the
integration with the IoT platform (Blynk app). The
selection of the visual stimulants (yellow, red, and green
LEDs) was controlled via the IoT-based stimulant
controller buttons, which were developed in the Blynk
app. Each time the visual stimulant is selected, the
reaction time in milliseconds will start counting until the
subject performs the front kick on the kick pad. The
vibration sensor will stop the reaction time counting
when it detects vibration while the force sensor will
measure the amount of force exerted by the athlete’s front
kick. The flexibility index will be calculated when the
data of the maximum kick range of the subject and the
height of the subject are inserted in the Blynk app on the
smartphone. Then, the data of reaction time, kick impact
force and flexibility index will be displayed on the
organic light-emitting diode (OLED) display and later
will be saved into the Blynk server. In addition, a reset
button widget in the Blynk app is used to reset the
previous data collected.
Fig. 1. The overall block diagram of IoT-based instrumentation.
A. Participant Consideration
This research work was approved by the Ethical
Research Committee from the Research Management
Centre (RMC) UTHM to conduct subject testing. A total
of four male subjects from the Silat background, which
weighted between 61kg to 80kg was recruited in this
study. All of them were high achievers in various Silat
tournaments at the university level. The subjects were
asked to fill in the consent form first before participating
in the SRT task. Next, the subjects were requested to
perform the front kick for three times trials to ensure the
data collected is valid and constant during the experiment
task. The front kick is one of the Silat kicking skills
usually performed during their training and, hence it has
been selected in this study.
B. IoT Blynk Application
Blynk is an IoT platform that can work with iOS and
Android operating systems. It allows controlling
electronic devices over the Internet. It provides a digital
dashboard, whereby users can build a graphic interface
using different widgets by the simple act of dragging and
dropping the widgets [8]. Blynk can also read, store and
display sensor data. There are three main components in
Blynk, which are the Blynk app, server, and libraries. The
Blynk app helps to build the interface for remote control,
as well as monitoring and the server provides
communication between the Blynk app and the hardware.
The libraries allow the communication for the hardware
with the server through the use of coding [9], [10].
In this research, there were several widgets selected to
control and monitor the reaction time, kick impact force,
and flexibility index parameters, which are Superchart
widget, Value Display widget, Numeric Input widget, and
Button widget. The Superchart and Value Display
widgets were used to monitor the data collected from the
sensor, whereas the Button widget was used to control the
visual LED stimulants (yellow, red, and green). The
numeric input widget was used to accept the key-in value
of the maximum kick range (cm) and the height of each
participant (cm) for the determination of the flexibility
index (FI) (dimensionless) [11], described as
Maximun kick range
FI Body height
=
Fig. 2 shows the coding that allows Arduino
NodeMCU to interface with the Blynk app. The #include
<ESP8266WiFi.h> is the NodeMCU library, which
enables Arduino NodeMCU to interface with the Blynk
app, whereas Blynk.begin(auth, ssid, pass) is a command
that enables Blynk to recognize the device and to initiate
the connection.
Fig. 2. Steps to initiate communication between Arduino NodeMCU
and the Blynk app.
Fig. 3 illustrates the flowchart of the IoT-based
instrumentation for reaction time, kick impact force, and
flexibility index. The communication between Arduino
NodeMCU and the Blynk enables the control of visual
stimulation at one’s fingertips. Through the Blynk app,
the LEDs can be triggered with a single tap on the button
widget. In this study, the Simple Reaction Time (SRT)
task is designed to assess the athlete's reaction time based
on the visual stimuli (yellow, red, or green LEDs) by
asking the athletes to perform the front kick on the
prototype as soon as the stimulus is presented.
Saved into
Blynk Server
Reaction
Time (ms)
Kick
Impact
Force (N)
Flexibility
Index
Stimulants
Controller Switch
via Blynk (IoT)
Output
Vibration
Sensor
Arduino
NodeMcu
Force
Sensor
Stimulants Output:
1.
Green LED or
2.
Red LED or
3.
Yellow LED
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
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Fig. 3. The flowchart of IoT-based instrumentation.
For example, when the red LED turns on (visual
stimulus is triggered), the subject will execute the front
kick on the kick pad. Then, the red LED will turn off as
soon as the vibration sensor detects the vibration that is
caused by the front kick impact. Next, the reaction time
will be measured between the starting time of the LED lit
up and the LED turn off time. Simultaneously, the force
sensor will detect the force of the kicking applied by the
subject. As for the flexibility index, the maximum kick
range of the subject and the subject's height will be
manually inserted into the Blynk app. The resulted
flexibility index as well as the reaction time and the
kicking force will be displayed on the OLED display and
the Blynk app. All the data will be saved into the Blynk
server for data storage. A reset button widget can be
triggered to reset the previous data collected for the next
trial.
C. Experimental Protocol for Simple Reaction Time (SRT)
Task
Fig. 4 shows the flowchart of the SRT experiment
protocol. First of all, the subjects were briefed on the
experiment protocol in detail. A written consent form was
distributed to all participants to fill up their personal
information required in the experiment. The athletes were
requested to do warm-up to prevent any injury from
happened during the experiment. This research consists
of a Simple Reaction Time (SRT) task, which requires
the participant to react towards the visual stimulus.
During the SRT task, the subject will stand on the starting
line, which is two meters away from the kick pad. Then,
the subject is required to focus on the visual stimuli and
react by performing the zigzag step before executing the
front kick on the kick pad when the stimuli are presented.
The visual stimuli will be randomly presented without
any notice, hence the subject has to be alert at all times.
After performing the first kicking, the subject must move
back to the starting line as soon as possible and wait for
the same stimulus to be presented again for the second
kicking. Thus, one trial of the experiment required the
subject to perform two kicking actions on the kick pad.
An average value was taken for each trial performed. For
the measurement of kick impact force, the participant was
required to kick on the force sensor attached to the kick
pad as shown in Fig. 5. Each participant was requested to
perform three trials for each stimulus. However, thirty
seconds of resting time were given to the subject before
proceeding to the next trial. A repetition of the trial would
be conducted should any technical problem occurred.
Fig. 4. The flowchart of SRT experiment protocol
III. RESULTS AND DISCUSSIONS
This section presents the IoT-based instrumentation
development and data analysis on the collected data
based on reaction time, kick impact force, and flexibility
index.
A. IoT-Based Instrumentation for Reaction Time, Kick
Impact Force, and Flexibility Index
Fig. 5 shows the front view of the IoT-based prototype
with the hardware design attached to the top surface of
the kick pad. The hardware components were placed in a
Perspex box and the box was adhesively attached to the
top surface of the kick pad. The OLED display and the
three LEDs (yellow, red, and green) were placed on the
top of the Perspex box, whereas the force sensor was
attached to the kicking surface of the kick pad that acted
as the target for the participant during the experiment.
Table I summarized the functions of each numbering item
corresponding to Fig. 5.
The reaction time,
kick impact force
and the flexibility
index recorded?
No
Yes
Yes
Any technical error
occurred or repetition
needed?
No
End
An average value of reaction
time, kick impact force and
flexibility index will be
calculated for the first and
second kicking
The subject moves back to
starting line and wait for the
second kicking when the same
visual stimulant light
The subject performs the
zigzag step before
executing a front kick on
the kick pad after the
visual stimulant light up
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
©2022 Int. J. Elec. & Elecn. Eng. & Telcomm.
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TABLE I: FUNCTION FOR EACH NUMBERING LABEL BASED ON FIG.5
No
Name
Descriptions
1
LEDs
Consists of yellow, red, and green LEDs, which
acts as visual stimuli
2
OLED
Display of reaction time, kick impact force,
and flexibility index values
3
Kick Pad
Standard kick pad size used by Silat athlete
in the experiment
4
Force
Sensor
Acts as a target for the subject to focus their front
kick and measurement of kick force
Fig. 5. The front view of the IoT-based prototype.
Fig. 6. The back view of the IoT-based prototype.
TABLE II: FUNCTION FOR EACH NUMBERING LABEL BASED ON FIG.6
No
Name
Descriptions
1
Vibration
Sensor
Used to detect vibration for reaction time
counting
2
Arduino
NodeMCU
The microcontroller is used to process the
input and the output of data as well as
communication with the Blynk platform
3
Power Bank
The power supply of 5V for Arduino
NodeMCU
Fig. 6 shows the back view of the IoT-based prototype
with the power supply attached to the back of the kick
pad. A power bank of five volts was used to supply
power for the Arduino NodeMCU. The Arduino
NodeMCU was soldered on the stripboard along with the
vibration sensor. The vibration sensor was used to sense
the vibration caused by the kicking force of the subject,
which triggered the reaction time to stop counting. Table
II summarized the functions of each numbering item
corresponding to Fig. 6.
Fig. 7 shows the resulted interface of the IoT
application through the Blynk app. There were several
main indicators such as the visual stimulant control
buttons which were used to control the visual stimuli
(yellow, red, and green LEDs), the reset button for
resetting the Blynk app, three main parameters displays;
reaction time, kick impact force as well as flexibility
index. It can be seen that the reaction time shown was
1106ms, whereas the kick impact force and the flexibility
index were displayed as 47.97N and 0.62 (dimensionless),
respectively. The same values displayed in the Blynk app
were also shown on the OLED display as depicted in Fig.
8.
Fig. 7. The Blynk app interface for the developed prototype.
Fig. 8. The organic light-emitting diode (OLED) display.
B. Analysis of Reaction Time, Kick Impact Force, and
Flexibility Index
The data analysis was carried out on four Silat athletes,
which were high achievers in the Silat tournament. Each
of them was weighted between 61kg to 80kg. In this
research, a small sample size of the subject was recruited
due to the insufficient number of Silat athletes available.
Nonetheless, the number of subjects was adequate
because the athletes were intended to test whether or not
the prototype could function as the initial expectation.
The subjects were required to conduct three trials of the
SRT experiment.
Fig. 9 shows the average means and standard deviation
of reaction time of the subjects in yellow, red, and green
LEDs. Based on Fig. 9, it can be seen that Subject D has
the fastest reaction time, which was 1762.8±89.7ms and
the slowest reaction time was exhibited by Subject B,
which was 2170±124.7ms in the yellow LED stimulus.
For the red LED stimulus category, Subject C has the
fastest reaction time, which was 1560±146.5ms and the
slowest reaction time came from Subject A, which was
2044.3±70.2ms. In the green LED stimulus category, it
can be deduced that Subject C has the fastest reaction
time, which was 1485.2±126.7ms, whereas Subject A has
the slowest reaction time, which was 2067±53.1ms.
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
©2022 Int. J. Elec. & Elecn. Eng. & Telcomm.
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Fig. 9. The average means and standard deviation of reaction time in
yellow, red, and green LEDs.
Fig. 10. The average means and standard deviation of kick impact force
in yellow, red, and green LEDs.
TABLE III: THE FLEXIBILITY INDEX OF EACH PARTICIPANT
No
Subject
Weight (kg)
Flexibility index
1
A
67
0.503
2
B
77
0.508
3
C
70
0.592
4
D
68
0.573
Fig. 10 illustrates the average means and standard
deviation of the kick impact force of the subjects based
on the visual stimuli (yellow, red, and green LEDs).
Subject C has the highest kick impact force, which was
466.6±19.8N as compared to others and the lowest kick
impact force came from Subject A, which was
367.7±21.7 N for the yellow LED stimulus. As for the red
LED stimulus, Subject C has the highest kick impact
force, which was 494.7±11N in comparison with others,
whereas Subject A has the lowest kick impact force of
376±36.1N. In the green LED stimulus category, Subject
C dominated its peer with the highest kick impact force
of 492.3±3.3N, whereas Subject A has the lowest kick
impact force of 374.9±31.5N.
The analysis of the subjects’ flexibility index had
shown that Subject C has the highest flexibility index,
which was 0.592 followed by Subject D (0.573), Subject
B (0.508), and Subject A has the lowest flexibility index
of 0.503 as tabulated in Table III. Higher flexibility index
indicated that one is more flexible as compared with
others [9]. In this case, Subject C was the most flexible,
whereas Subject A was the least flexible among the
subjects.
To sum up the analysis, Subject C has the overall
fastest reaction time in the SRT task with the fastest
reaction time achieved in green LED stimulus, which was
1485.2±126.7ms. In comparison, Subject A has the
slowest reaction time throughout the SRT task with the
slowest reaction time achieved, which was 2044.3±70.2
ms in red LED stimulus. Meanwhile, Subject C also
dominated its peer in kick impact force measurement
with the highest impact force of 494.7±11N in red LED
stimulus. In contrast, Subject A possessed the lowest kick
impact force in the whole SRT task with the lowest kick
impact force of 367.7±21.7N in yellow LED stimulus.
The deduced reason why Subject A obtained the lowest
kick impact force might perhaps be due to the weight
difference among the subjects whereby Subject A has the
lowest weight of 67kg as compared with Subject B (77
kg), Subject C (70kg), and Subject D (68kg). For the
flexibility index, Subject C has the highest flexibility
index of 0.592, which indicated that Subject C was the
most flexible in comparison with the others. Thus, to
summarize all the analyses, Subject C has the fastest
reaction time, highest kick impact force, and the most
flexible among the subjects recruited, whereas Subject A
has the slowest reaction time, lowest kick impact force,
and lowest flexibility index throughout the whole SRT
task. In addition, Subject C achieved the highest kick
impact force might be due to the highest flexibility of the
leg as there was a significant relationship between the
force of the leg and the leg’s flexibility [12]. Besides that,
it can be concluded that 75 % of the participants were
more responsive towards green LED as compared with
yellow and red LEDs in the SRT task in terms of reaction
time. This result corresponded to the findings from the
previous studies [13], [14], which indicated that the
reaction for green color stimuli was significantly less than
the reaction time for red color stimuli. The deductions on
why most of the subjects responded faster towards the
green LED are the green light has a shorter wavelength
and carries greater energy than the yellow and red lights
in the same quantum [14]. However, another study, which
presented inconsistent results that showed the reaction
time for red color stimuli was significantly less compared
to green color stimuli [15]. Therefore, further research
would be needed to assess the effect of colored stimuli on
the reaction time of the athletes. Last but not least, this
developed instrument provides IoT functions, which
enabled remote control of the device during the SRT task
and the data collected can be saved in the Blynk server,
besides measuring reaction time, kick impact force, and
flexibility index.
IV. CONCLUSION
In conclusion, an IoT-based instrumentation prototype
for measuring the reaction time, kick impact force, and
flexibility index of Silat athletes based on Simple
Reaction Time (SRT) task was successfully developed.
The developed device was able to measure the reaction
time, kick impact force, and flexibility index of each
subject, and the data was successfully saved in the Blynk
server for later analysis. There are some limitations,
which cause the collected data to be inconclusive. Firstly,
the physical and mental states play an important factor in
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
©2022 Int. J. Elec. & Elecn. Eng. & Telcomm.
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collecting the reaction time, kick impact force, and
flexibility index data. Some of the subjects could only
participate in the experiment after their busy schedules.
Secondly, this study recruited a small sample size of
subjects. Nevertheless, this sample size was sufficient
since the subjects were recruited to test the performance
of the developed prototype. Lastly, it can be concluded
that 75% of the participants were more responsive
towards green LED as compared with yellow and red
LEDs in the SRT task in terms of reaction time. As for
future works, several recommendations can be done.
Firstly, the design of the hardware in terms of circuitry
and casing can be improved for a better prototype
presentation. Secondly, a big sample size of subjects and
different branches of combat sports can be recruited to
assist in further analysis. Furthermore, the sound stimulus
can be included in future studies. Lastly, further research
should be conducted to assess the effect of colored
stimuli on the reaction time of the athletes.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
Dr. Nur Anida led the conducted research and edited
the paper. Mr. Ng Chun Keat developed the prototype,
analyzed the data, and wrote the paper. All authors had
approved the final version of the paper.
ACKNOWLEDGMENT
The authors would like to express deep gratitude to
Research Management Centre (RMC), Universiti Tun
Hussein Onn Malaysia (UTHM) for funding the journal’s
fee and monthly financial support via GPPS Grant No.
H314 and TIER 1 Grant No. H226.
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Copyright © 2022 by the authors. This is an open-access article
distributed under the Creative Commons Attribution License (CC BY-
NC-ND 4.0), which permits use, distribution, and reproduction in any
medium, provided that the article is properly cited, the use is non-
commercial and no modifications or adaptations are made.
Mr. Chun Keat Ng is a postgraduate student
at Universiti Tun Hussein Onn Malaysia
(UTHM). His research interests: biomedical
engineering, sports sciences, and the
application of the Internet of Things (IoT).
Dr. Nur Anida Jumadi is a lecturer in the
Department of Electronic Engineering,
Faculty of Electrical and Electronic
Engineering, Universiti Tun Hussein Onn
Malaysia (UTHM). Her research interests:
multidisciplinary research related to
biomedical optics (development of optical
sensors), optical simulation, medical signal
processing, biomedical rehabilitation devices,
sports engineering, and the application of the
Internet of Things (IoT) in the medical electronics field.
International Journal of Electrical and Electronic Engineering & Telecommunications Vol. 11, No. 1, January 2022
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