Capturing Human Movements in Real Time in Collaborative Robots Workspace within Industry 5.0

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

232

Capturing Human Movements in Real Time in Collaborative Robots

Workspace within Industry 5.0

Vladyslav Yevsieiev 1, Svitlana Maksymova 1, Ahmad Alkhalaileh 2

1 Department of Computer-Integrated Technologies, Automation and Robotics,

Kharkiv National University of Radio Electronics, Ukraine

2 Senior Developer Electronic Health Solution, Amman, Jordan

Abstract:

The article examines the application of technologies for capturing

human movements in real time in collaborative robots workspace in the context of
Industry 5.0. Mathematical apparatus and software for analyzing movements with high
accuracy has been developed, which allows to increase the safety and efficiency of
human-robot interaction. The conducted experiments showed the influence of lighting
conditions and movements speed on the accuracy of movements capture and
visualization, which is critically important for the optimal operation of collaborative
robots in production environments.

Key words:

Real-time motion capture, collaborative robots, human-robot

interaction, Industry 5.0, pose estimation, workplace safety

Introduction

Capturing human movements in real time in the collaborative robot workspace

manipulators is of particular importance in the context of the development of
Industry 5.0, where the main emphasis is placed on the harmonious interaction of
humans and robots [1]-[15]. Industry 5.0 differs from the previous paradigm of Industry
4.0 in that it strives for a closer integration of human creativity and intelligence with
automated systems, increasing the efficiency, flexibility and adaptability of production
processes. Capturing human movements in the work area allows manipulator robots to
respond to operator actions in real time, ensuring safe and efficient collaboration [16]-
[20]. This is important for tasks such as joint assembly of complex components,
performing delicate operations that require precision and human-machine interaction.

The study of this technology is relevant due to the need to increase the level of

automation without losing human control, as well as with the aim of improving working

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

233

conditions, reducing operator fatigue and ensuring his safety. Different methods and
approaches can be used here [21]-[36]. Ensuring safety is a key issue in developing
collaborative robotsWithin Industry 5.0, it is important that such technologies
contribute to the development of personalized production, where robots are able to
adapt to the individual needs and work style of each operator, which makes research in
this area extremely relevant.

Related works

With the advent of Industry 5.0, the problem of ensuring safety has become

especially acute. Accordingly, human detection in the robot's workspace has come to
the fore. And it is natural that more and more scientists are devoting their works to this
problem. Let's consider several recent such works.

Let us begin with the work [37]. There is noted that industrial robots especially

in human–robot collaboration settings can be hazardous if safety is not addressed
properly. Human–robot collaboration systems can be a tremendously complex process;
therefore, proper safety mechanisms must be addressed at an early stage of
development.

Bonci, A., and co-authors in [38] write that if the robot is not aware of the human

position and intention, a shared workspace between robots and humans may decrease
productivity and lead to human safety issues. So, their study [38] presents a survey on
sensory equipment useful for human detection and action recognition in industrial
environments.

Rahmaniar, W., & Hernawan, A. in [39] propose to use SSD MobileNet V2

model that provides the highest accuracy with the fastest computation time compared
to other models in our video datasets with several scenarios in order to detect an object
such as a human.

The paper [40] researches hand gestures that are quite suitable for space human–

robot interaction because of their natural and convenient features. But hand gestures
are very complicated and hand sizes are very small in some images. These problems
make the robust real-time hand detection and localization very difficult.

Scientists in [41] introduce a new feature to improve human classification in

sparse, long-range point clouds. They present a system for online learning of human

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

234

classifiers by mobile service robots using 3D LiDAR sensors, and its experimental
evaluation in a large indoor public space.

The article [42] notes that presence of workers in the shared workspace with

robots decreases the productivity, as the robot is not aware about the human position
and intention, which leads to concerns about human safety. This issue is addressed in
this work by designing a reliable safety monitoring system for collaborative robots
(cobots).

Researchers in [43] propose a multilayer feedforward neural network-based

approach for human–robot collision detection taking safety standards into
consideration.

Liu, C., & Szirányi, T. in [44] consider the problem of real-time UAV human

detection and recognition of div and hand rescue gestures. They use div-featuring
solutions to establish biometric communications, like yolo3-tiny for human detection.

Thus, we see that the problem of human detection and understanding of his

gestures is extremely relevant. Further in our article we will present a study of the
dependence of detection quality on lighting, as well as gesture recognition on the speed
of their execution.

Capturing human movements in real time mathematical model.

The mathematical model of capturing human movements with the coordinates

calculation in the frame in real time based on computer vision systems can be described
as a sequence of stages.

The first stage, receiving a video stream from the camera installed on the gripping

device of the robot. A video stream can be represented as the following discrete
sequence of frames

V(t) = {F

1

,F

2

,…,F

n

}, F

i

=f(t

i

)

(1)

V(t)

– video stream;

F

i

– a single frame received at a point in time

t

i

;

f(t

i

)

– a function that describes a frame at time.

The next stage is frame conversion. Each frame

F

i

is first converted to a

coordinate system suitable for further processing (for example, RGB or grayscale):

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

235

F

i

RGB

=T(F

i

)

(2)

T(F

i

)

– the frame conversion operator to the desired color space, which represents

each pixel of the frame as a vector

P(x,y)

in the color space.

After that, it is necessary to determine the key points (Landmarks Detection).

The model of the human div can be described through a set of key points (landmarks),
which represent joints or other characteristic points. Let the computer vision system
determine the

N

key points, for each of which the coordinates in the frame space are

calculated:

L

i

={(x

i

,y

i

)}, i=1,2,…,N

(3)

L

i

– point

i

coordinates;

(x

i

,y

i

)

– coordinates of this point on the frame plane (in pixels).

To generalize the position of the div in relative coordinates, the position of key

points is normalized:

x

i

'

=x

i

/W, y

i

'

=y

i

/H

(4)

W

and

H

– width and height of the frame, respectively;

x

i

'

and

y

i

'

– normalized point coordinates.

Distances between key points, which can be calculated using the Euclidean

metric, are important for motion analysis:

𝑑

𝑖𝑗

= √(𝑥

𝑖

− 𝑥

𝑗

)

2

+ (𝑦

𝑖

− 𝑦

𝑗

)

2

(5)

d

ij

– distance between points

i

and

j.

In order to smooth the movement, which can be "noisy" due to small deviations

or errors in the frames, filtering is used. For example, smoothing using an exponential
moving average:

x

i

”=ax

i

(t)+(1-a)x

i

(t-1)

(6)

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

236

a

– коефіцієнт згладжування;

x

i

”

– the smoothed value of the coordinate at the moment of time

t

;

x

i

(t)

– coordinate at a moment in time

t

.

Human movement can also be described through the rate of change in the

position of key points in time.

Point movement speed:

v

i

(t)= (x

i

(t)-x

i

(t-1))/Δt

(7)

Δt

– time interval between frames.

Acceleration is calculated as a change in velocity:

a

i

(t)= (v

i

(t)-v

i

(t-1))/Δt

(8)

After all the calculations, the coordinates of each point in the frame space can be

represented as:

C={(x

1

’

,y

1

’

), (x

2

’

,y

2

’

),…, (x

N

’

,y

N

’

)}

(9)

𝐶

– a set of coordinates of all key points.

The presented model covers the process of capturing human movements in real

time, starting with the processing of the video stream and ending with the div key
points coordinates calculation. This model can be used to control manipulator robots,
responding to human movements and ensuring effective cooperation in the workspace.

Development of a program for testing the human motion capture model in

real time and conducting experiments

The choice of Python as the language for developing a real-time human motion

capture model testing program is due to its versatility and ease of working with
computer vision libraries such as OpenCV and MediaPipe [45]. Python supports easy
integration with machine learning algorithms, allowing rapid prototyping and testing of

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

237

models. High code readability and a large amount of documentation simplify the
process of developing and debugging programs. As an integrated development
environment (IDE), PyCharm provides convenient tools for writing code, debugging,
and working with libraries, making it an ideal choice for Python projects. PyCharm
supports virtual environments, making it easier to manage dependencies and simplify
project setup to work with real-time models. The combination of Python and PyCharm
makes it possible to quickly develop, test and scale high-accuracy human motion
capture applications.

We will give a description of some fragments of the software code from

implementation of capturing human movements mathematical model in real time.

frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(frame_rgb)
frame_pose = frame.copy()
This piece of code is necessary to correctly capture human poses from a video

stream using MediaPipe. First, the frame read by OpenCV in BGR format is converted
to RGB, since MediaPipe works with this format. The frame is then processed for pose
analysis, where MediaPipe identifies key points on the human div. After that, a copy
of the frame is created, on which the processing results will be superimposed, for
example,

a

human

skeleton,

without

changing

the

original

image.if

results.pose_landmarks:

# Output of coordinates of all points (e.g. point #0 — nose)
for idx, landmark in enumerate(results.pose_landmarks.landmark):
h, w, _ = frame.shape
cx, cy = int(landmark.x * w), int(landmark.y * h)
This piece of code is used to check if MediaPipe was able to find div landmarks

(pose_landmarks) in the frame, and if so, it outputs the coordinates of all those points.
It loops through each skeleton point, getting its normalized (x,y) coordinates and
converting them to pixel coordinates based on the frame dimensions (height and width).
This allows you to determine the exact location of each key point in the image, for
example, the coordinates of the nose or other div parts.

# Output the coordinates in the frame
cv2.putText(frame_pose, f'ID {idx}: ({cx}, {cy})', (cx, cy),

cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

238

This piece of code is used to output text information on a video frame that shows

the index and coordinates of each key point on the div (for example, the coordinates
of the nose or other div parts). The text with the index of the point and its coordinates
is applied directly to the image in the corresponding position using the font and color
(in this case blue). This helps visualize the location and ID of each point on the human
div in the frame.

# Drawing a skeleton on the frame
mp_drawing.draw_landmarks(frame_pose, results.pose_landmarks,

mp_pose.POSE_CONNECTIONS)

This piece of code is used to render a human skeleton on a frame by drawing key

div points and their connections using the MediaPipe library. Using the processing
results (pose_landmarks), the draw_landmarks function draws lines and points
representing the joints and connections between them on the image. This allows you to
graphically display a person's pose in real time, showing their skeletal structure directly
on the video frame.

# Output of two windows: original video and with poses
cv2.imshow('Real-time Video', frame)
cv2.imshow('Pose Estimation', frame_pose)
This code fragment is responsible for the simultaneous display of two windows:

one with the original video stream and the other with a video on which visualization of
the human skeletal structure is superimposed. This allows the user to see both the raw
video and real-time human pose estimation results. In this way, the user can compare
the original video material with the video, on which the key points and the connections
between them are marked.

The results of the developed program for capturing human movements in real

time in the Python language in the PyCharm development environment are shown in
Figure 1.

Let us conduct a series of experiments to evaluate the parameters of the

developed program for the implementation of the human motion capture model in real
time:

- lighting effects research [46], i.e. experiments in different lighting conditions

to evaluate how it affects the accuracy of motion capture and visualization of key
points;

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

239

- analysis of speed and accuracy, study of the delay between human movements

and their reflection in the system, and also evaluate the accuracy of detecting
coordinates for fast or complex movements.

a)

b)

a) Real-time Video Window; b) Pose Estimation Window

Figure 1:

Developed program for capturing human movements in real time

results

Table 1 shows the results of experiments on the study of the influence of lighting

on the capturing human movements in real time accuracy.

Table 1:

Results of experiments on the study of the lighting influence on the

capturing human movements in real time accuracy of

Lighting

conditions

Brightn

ess (lux)

Detect

ion accuracy

(%)

Movemen

ts visualization

Notes

Bright

light

800

95

Clear, no

artifacts

Optimal

conditions

Moderate

light

500

87

Good,

possible artifacts

Some

points may be

omitted

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

240

Weak

light

200

73

Blurry,

unclear points

Significa

nt loss of

accuracy

Low

light

100

61

Very

blurry

High

probability of

errors

Strobe

light

-

51

Unpredict

able

Deteriora

tion of

visualization

Multicol

ored light

-

62

Inconstant

Effect of

colors on

accuracy

The obtained results of the experiment on the study of the effect of lighting on

the accuracy of capturing human movements in real time are presented in Figure 2.

Figure 2:

Combined graph of the effect of illumination on the accuracy of

human motion capture in real time

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

241

The obtained results of the study of the lighting influence on the movement

capture accuracy demonstrate a clear relationship between the intensity of light and the
quality of detecting the positions of key points. In bright conditions (800 lux), the
system shows high accuracy (95%) with clear visualization, which confirms optimal
conditions for work. In moderate light (500 lux), the accuracy drops to 87% and
artifacts appear, indicating that some points are lost. In weak and low light (200-
100 lux), the accuracy drops sharply to 73% and 61%, respectively, which is
accompanied by blurring and an increased probability of errors. Stroboscopic and
multi-colored lighting also significantly degrade accuracy (51% and 62%) due to
rendering instability and the effect of colors on the algorithm.

Table 2 shows the results of experiments on the speed and accuracy of the real-

time motion capture program, with an emphasis on the delay between human
movements and their reflection in the system, as well as on the accuracy of detecting
coordinates in complex or fast movements.

The obtained results of the experiment on the study of the speed and accuracy of

the real-time motion capture program, with an emphasis on the delay between human
movements and their reflection in the system, as well as on the accuracy of detecting
coordinates in complex or fast movements, are presented in Figure 3.

The results of the study of the speed and accuracy of the real-time motion capture

program show a clear correlation between the complexity and speed of movements and
the delay of their display in the system.

Table 2:

Results of experiments on the speed and accuracy of the real-time

motion capture program.

Movement type

Average

latency (ms)

Accuracy

of coordinate

detection (%)

Notes on

movement complexity

Slow, simple

movements

47

98

-

Moderate,

medium difficulty

movements

58

93

Slight delay,

accuracy decreases

slightly

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

242

Fast, medium

difficulty movements

94

83

Noticeable delay,

possible inaccuracies

Very fast

movements

128

69

Significant delay,

accuracy noticeably

deteriorates

Complex,

unpredictable

movements

164

53

High latency, low

accuracy

Figure 3:

Combined graph of the study of the speed and accuracy of the real-

time motion capture program

For slow and simple movements, the average delay is 47 ms with a high accuracy

of coordinate detection (98%), which indicates an almost instantaneous response of the
system. With moderate movements, the delay increases to 58ms, and the accuracy drops
to 93%, indicating minor errors. Fast movements cause a noticeable increase in latency
to 94 ms and a drop in accuracy of up to 83%. For very fast and complex movements,
the delay reaches 128-164 ms, while the accuracy drops to 69% and 53%, respectively,
which indicates significant errors in coordinate processing under such conditions.

Conclusion

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

243

As part of the study of models, methods and software for capturing human

movements in real time in the collaborative robot workspace within Industry 5.0, the
importance and effectiveness of using computer vision systems for detecting and
analyzing human movements is considered. Modern technologies allow integrating
solutions on based on computer vision to improve human-robot collaboration. In the
context of Industry 5.0, special attention is paid to the safety and accuracy of the
interaction, which is made possible by the real-time analysis of the movements of the
workers human with high accuracy thanks to MediaPipe and Python in the PyCharm
environment.

Based on experiments, it was determined that lighting and movement speed

significantly affect the quality of capture. In bright conditions, accuracy reached 95%,
while in low light, accuracy dropped to 61%, and under strobe light, accuracy
deteriorated further. Experiments with different types of movements have also shown
that slow and simple movements provide maximum accuracy and minimum latency.
However, with very fast and complex movements, the delay reached 164 ms, which
negatively affects the accuracy of coordinate detection.

The developed software demonstrated its effectiveness in capturing human

movements, but showed some limitations in difficult environments. The accuracy of
the system depends on the quality of the lighting and the complexity of the movements,
which can be critical when working with collaborative robots. In general, the
integration of such technology into Industry 5.0 helps to increase the safety, accuracy
and efficiency of production processes, which is confirmed by the conducted
experiments and the analysis of the obtained results.

References:

1.

Samoilenko, H., & et al. (2024). Review for Collective Problem-Solving

by a Group of Robots. Journal of Universal Science Research, 2(6), 7-16.

2.

Yevsieiev, V., & et al. (2024). Route constructing for a mobile robot based

on the D-star algorithm. Technical Science Research in Uzbekistan, 2(4), 55-66.

3.

Abu-Jassar, A., & et al. (2023). Obstacle Avoidance Sensors: A Brief

Overview.

Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

244

4.

Gurin, D., & et al. (2024). Using the Kalman Filter to Represent

Probabilistic Models for Determining the Location of a Person in Collaborative Robot
Working Area. Multidisciplinary Journal of Science and Technology, 4(8), 66-75.

5.

Yevsieiev, V., & et al. (2024). The Canny Algorithm Implementation for

Obtaining the Object Contour in a Mobile Robot’s Workspace in Real Time. Journal of
Universal Science Research, 2(3), 7–19.

6.

Gurin, D., & et al. (2024). MobileNetv2 Neural Network Model for

Human Recognition and Identification in the Working Area of a Collaborative Robot.
Multidisciplinary Journal of Science and Technology, 4(8), 5-12.

7.

Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O.

(2020). Some features of route planning as the basis in a mobile robot. International
Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

8.

Matarneh, R., Maksymova, S., Deineko, Z., & Lyashenko, V. (2017).

Building robot voice control training methodology using artificial neural net.
International Journal of Civil Engineering and Technology, 8(10), 523-532.

9.

Nevliudov, I., & et al.. (2020). Method of Algorithms for CyberPhysical

Production Systems Functioning Synthesis. International Journal of Emerging Trends
in Engineering Research, 8(10), 7465-7473.

10.

Mustafa, S. K., Yevsieiev, V., Nevliudov, I., & Lyashenko, V. (2022).

HMI Development Automation with GUI Elements for Object-Oriented Programming
Languages Implementation. SSRG International Journal of Engineering Trends and
Technology, 70(1), 139-145.

11.

Nevliudov, I., Yevsieiev, V., Lyashenko, V., & Ahmad, M. A. (2021).

GUI Elements and Windows Form Formalization Parameters and Events Method to
Automate the Process of Additive Cyber-Design CPPS Development. Advances in
Dynamical Systems and Applications, 16(2), 441-455.

12.

Lyashenko, V., Abu-Jassar, A. T., Yevsieiev, V., & Maksymova, S.

(2023). Automated Monitoring and Visualization System in Production. International
Research Journal of Multidisciplinary Technovation, 5(6), 9-18.

13.

Abu-Jassar, A. T., Attar, H., Lyashenko, V., Amer, A., Sotnik, S., &

Solyman, A. (2023). Access control to robotic systems based on biometric: the
generalized model and its practical implementation. International Journal of Intelligent
Engineering and Systems, 16(5), 313-328.

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

245

14.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023).

Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic
Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

15.

Ahmad, M. A., Sinelnikova, T., Lyashenko, V., & Mustafa, S. K. (2020).

Features of the construction and control of the navigation system of a mobile robot.
International Journal of Emerging Trends in Engineering Research, 8(4), 1445-1449.

16.

Abu-Jassar, A., & et al. (2024). The Optical Flow Method and Graham’s

Algorithm Implementation Features for Searching for the Object Contour in the Mobile
Robot’s Workspace. Journal of Universal Science Research, 2(3), 64-75.

17.

Gurin, D., & et al. (2024). Effect of Frame Processing Frequency on

Object Identification Using MobileNetV2 Neural Network for a Mobile Robot.
Multidisciplinary Journal of Science and Technology, 4(8), 36-44.

18.

Yevsieiev, V., & et al. (2024). Object Recognition and Tracking Method

in the Mobile Robot’s Workspace in Real Time. Technical science research in
Uzbekistan, 2(2), 115-124.

19.

Gurin, D., & et al. (2024). Using Convolutional Neural Networks to

Analyze and Detect Key Points of Objects in Image. Multidisciplinary Journal of
Science and Technology, 4(9), 5-15.

20.

Yevsieiev, V., & et al. (2024). Using Contouring Algorithms to Select

Objects in the Robots’ Workspace. Technical science research in Uzbekistan, 2(2), 32–
42.

21.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021).

Neural networks as a tool for pattern recognition of fasteners. International Journal of
Engineering Trends and Technology, 69(10), 151-160.

22.

Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021).

Some Features of Classifiers Implementation for Object Recognition in Specialized
Computer systems. TEM Journal: Technology, Education, Management, Informatics,
10(4), 1645-1654.

23.

Deineko, Zh., & et al.. (2021). Color space image as a factor in the choice

of its processing technology. Abstracts of I International scientific-practical conference
«Problems of modern science and practice» (September 21-24, 2021). Boston, USA,
pp. 389-394.

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

246

24.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General

methodology for implementation of image normalization procedure using its wavelet
transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

25.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast

modification as a tool to study the structure of blood components. Journal of
Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-
160.

26.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for

detection and diagnosis of fish diseases. International Journal of Advanced Research in
Computer Science and Software Engineering, 6(7), 79-83.

27.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016).

Contour Detection and Allocation for Cytological Images Using Wavelet Analysis
Methodology. International Journal, 4(1), 85-94.

28.

Mousavi, S. M. H., Lyashenko, V., & Prasath, S. (2019). Analysis of a

robust edge detection system in different color spaces using color and depth images.
Компьютерная оптика, 43(4), 632-646.

29.

Orobinskyi, P., Deineko, Z., & Lyashenko, V. (2020). Comparative

Characteristics of Filtration Methods in the Processing of Medical Images. American
Journal of Engineering Research, 9(4), 20-25.

30.

Orobinskyi, P., Petrenko, D., & Lyashenko, V. (2019, February). Novel

approach to computer-aided detection of lung nodules of difficult location with use of
multifactorial models and deep neural networks. In 2019 IEEE 15th International
Conference on the Experience of Designing and Application of CAD Systems
(CADSM) (pp. 1-5). IEEE.

31.

Lyashenko, V., Kobylin, O., & Selevko, O. (2020). Wavelet analysis and

contrast modification in the study of cell structures images. International Journal of
Advanced Trends in Computer Science and Engineering, 9(4), 4701-4706.

32.

Matarneh, R., & et al.. (2019). Development of an Information Model for

Industrial Robots Actuators. IOSR Journal of Mechanical and Civil Engineering
(IOSR-JMCE), 16(1), 61-67.

33.

Sotnik, S., & et al.. (2022). Analysis of Existing Infliences in Formation

of Mobile Robots Trajectory. International Journal of Academic Information Systems
Research, 6(1), 13-20.

ISSN (E): 2181-4570 ResearchBib Impact Factor: 6,4 / 2023 SJIF 2024 = 5.073/Volume-2, Issue-10

247

34.

Sotnik, S., & et al.. (2022). Modern Industrial Robotics Industry.

International Journal of Academic Engineering Research, 6(1),. 37-46.

35.

Drugarin, C. V. A., Lyashenko, V. V., Mbunwe, M. J., & Ahmad, M. A.

(2018). Pre-processing of Images as a Source of Additional Information for Image of
the Natural Polymer Composites. Analele Universitatii'Eftimie Murgu', 25(2).

36.

Lyubchenko, V., Veretelnyk, K., Kots, P., & Lyashenko, V. (2024).

Digital image segmentation procedure as an example of an NP-problem.
Multidisciplinary Journal of Science and Technology, 4(4), 170-177.

37.

Arents, J., & et al. (2021). Human–robot collaboration trends and safety

aspects: A systematic review. Journal of Sensor and Actuator Networks, 10(3), 48.

38.

Bonci, A., & et al. (2021). Human-robot perception in industrial

environments: A survey. Sensors, 21(5), 1571.

39.

Rahmaniar, W., & Hernawan, A. (2021). Real-time human detection using

deep learning on embedded platforms: A review. Journal of Robotics and Control
(JRC), 2(6), 462-468.

40.

Gao, Q., & et al. (2020). Robust real-time hand detection and localization

for space human–robot interaction based on deep learning. Neurocomputing, 390, 198-
206.

41.

Yan, Z., & et al. (2020). Online learning for 3D LiDAR-based human

detection: experimental analysis of point cloud clustering and classification methods.
Autonomous Robots, 44(2), 147-164.

42.

Mohammadi Amin, F., & et al. (2020). A mixed-perception approach for

safe human–robot collaboration in industrial automation. Sensors, 20(21), 6347.

43.

Sharkawy, A. N., & et al. (2020). Human–robot collisions detection for

safe human–robot interaction using one multi-input–output neural network. Soft
Computing, 24(9), 6687-6719.

44.

Liu, C., & Szirányi, T. (2021). Real-time human detection and gesture

recognition for on-board UAV rescue. Sensors, 21(6), 2180.

45.

Yevsieiev, V., & et al. (2024). Building a traffic route taking into account

obstacles based on the A-star algorithm using the python language. Technical Science
Research In Uzbekistan, 2(3), 103-112.

46.

Vizir, Y., & et al. (2024). Lighting Control Module Software

Development. Journal of Universal Science Research, 2(2), 29–42.