Vol. 2 No. 9 (2024), Articles
Vol. 2 No. 9 (2024)

Using LSTM Recurrent Neural Networks to Predict the Trajectory of Human Hand Movement in the Working Area of a Collaborative Robot-Manipulator

Articles
Published 2024-09-06
Svitlana Maksymova
Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Ahmad Alkhalaileh
Senior Developer Electronic Health Solution, Amman, Jordan
Dmytro Gurin
Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
Vladyslav Yevsieiev
Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv National University of Radio Electronics, Ukraine
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DOI

Keywords

Industry 5.0
Collaborative Robot
Work Area
Computer Vision
LSTM,
Trajectory Prediction

How to Cite

Using LSTM Recurrent Neural Networks to Predict the Trajectory of Human Hand Movement in the Working Area of a Collaborative Robot-Manipulator . (2024). Journal of Universal Science Research, 2(9), 17-32. https://universalpublishings.com/index.php/jusr/article/view/7024
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Abstract

The article examines the use of LSTM recurrent neural networks for predicting the trajectory of human hand movement in the working area of a collaborative robot-manipulator. The results demonstrate high prediction accuracy for slow movements, but reveal certain limitations for fast and complex trajectories. The proposed approach is aimed at improving the safety and efficiency of the joint work of humans and robots within the framework of the concept of Industry 5.0.

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References

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