International Peer-Reviewed JournalOpen AccessISSN 2456-8880
irejournals@gmail.com+91-7433024337

Home / Current Issue / Paper 1710076

1710076 Vol 9 · Issue 2 Download Paper

Modeling a Gesture Sensing Robot Using Arduino

Richard Ikechukwu Success Comfort C. Olebara

Subject area: Science,Engineering and Technology  ·  Area of research: Human-Machine Interfaces

Abstract

This study presents the modeling of an affordable gesture sensing robot using Arduino microcontrollers to enable intuitive human-robot interaction. The research addresses the limitations of traditional robotic control systems by introducing a cost-effective, real-time gesture recognition system that translates hand movements into robotic actions with over 90% accuracy and minimal latency. The methodology integrates Structured Systems Analysis and Design Methodology (SSADM), iterative prototyping, and experimental validation. Arduino Uno and Nano boards, coupled with MPU6050 sensors, facilitate gesture detection. Signal processing algorithms were programmed in C++ on Arduino IDE. Performance results demonstrate system responsiveness within 100ms and operational wireless control over a 10?15 meter range, making it a viable educational and assistive robotics platform.

Keywords

Arduino, Gesture Recognition, Human-Robot Interaction, MPU6050.

References

[1] S. S. Rautaray and A. Agrawal, “Vision-based hand gesture recognition for human–computer interaction: A survey,” Artificial Intelligence Review, vol. 43, no. 1, pp. 1–54, 2015.

[2] Y. Chen, H. Zhang, and L. Guo, “Vision-based hand gesture recognition: A review,” Computers & Electrical Engineering, vol. 83, p. 106582, 2020.

[3] H. Kaur and A. Arora, “Real-time gesture recognition using IMU sensors,” Procedia Computer Science, vol. 167, pp. 2121–2130, 2020.

[4] M. Banzi and M. Shiloh, Getting Started with Arduino, 3rd ed., Maker Media, Inc., 2014.

[5] S. Rani, N. Kaur, and P. Kaur, “Arduino-based hand gesture-controlled robot,” International Journal of Engineering Research & Technology (IJERT), vol. 10, no. 3, pp. 541–546, 2021.

[6] J. Kim, J. Song, and C. Lee, “Sensor-fused gesture recognition using wearable devices,” Sensors, vol. 20, no. 8, p. 2345, 2020.

[7] P. Kumar and A. Singh, “Low-cost wireless hand gesture recognition system using Arduino,” IEEE International Conference on Emerging Trends in Engineering, pp. 112–117, 2019.

[8] R. C. Luo and O. Chen, “Mobile robot human-robot interaction by gesture recognition,” IEEE International Conference on Robotics and Automation, pp. 3670–3675, 2015.

[9] D. C. Luvizon, D. Picard, and H. Tabia, “2D/3D pose estimation and action recognition using multitask deep learning,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 5137–5146, 2018.

[10] T. M. Mitchell, Machine Learning, New York, NY, USA: McGraw-Hill, 1997.

[11] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.

[12] K. H. Thang, K. L. Hua, and S. C. Hidayat, “A wearable device for real-time hand gesture recognition,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 4, pp. 45–53, 2018.

[13] A. Khan, A. Sharma, and M. Goyal, “Comparative study of accelerometer and vision-based hand gesture recognition systems,” Procedia Computer Science, vol. 132, pp. 677–684, 2018.

[14] G. C. Burdea, “Invited review: The synergy between virtual reality and robotics,” IEEE Transactions on Robotics and Automation, vol. 15, no. 3, pp. 400–410, 1999.

[15] M. F. Al-Hameed, H. R. Al-Rizzo, and F. Al-Turjman, “A survey on Arduino-based educational robotics in STEM education,” Education and Information Technologies, vol. 26, no. 4, pp. 4243–4271, 2021.

[16] R. V. Kulkarni, “Real-time hand gesture recognition using MPU6050 and Arduino,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 7, no. 5, pp. 2231–2238, 2019.

[17] D. Xu, “A neural network approach for hand gesture recognition in virtual reality driving training system of SPG,” IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews, vol. 31, no. 4, pp. 440–448, 2001.

[18] M. Zhang, Z. Zhang, and J. Wu, “A survey on human-computer interaction using hand gesture recognition,” Journal of Physics: Conference Series, vol. 1237, no. 2, p. 022067, 2019.

How to cite this paper

Richard Ikechukwu Success, Comfort C. Olebara "Modeling a Gesture Sensing Robot Using Arduino" Iconic Research And Engineering Journals Volume 9 Issue 2 2025 Page 1308-1313
Richard Ikechukwu Success, Comfort C. Olebara "Modeling a Gesture Sensing Robot Using Arduino" Iconic Research And Engineering Journals, vol. 9, no. 2, Aug. 2025
Richard Ikechukwu Success, Comfort C. Olebara (2025). Modeling a Gesture Sensing Robot Using Arduino. Iconic Research And Engineering Journals, 9(2).
Richard Ikechukwu Success, Comfort C. Olebara "Modeling a Gesture Sensing Robot Using Arduino" Iconic Research And Engineering Journals, vol. 9, no. 2, Aug. 2025.
@article{1710076,
      author = {Richard Ikechukwu Success, Comfort C. Olebara},
      title = {Modeling a Gesture Sensing Robot Using Arduino},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {2},
      pages = {1308-1313},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710076.pdf},
      abstract = {This study presents the modeling of an affordable gesture sensing robot using Arduino microcontrollers to enable intuitive human-robot interaction. The research addresses the limitations of traditional robotic control systems by introducing a cost-effective, real-time gesture recognition system that translates hand movements into robotic actions with over 90% accuracy and minimal latency. The methodology integrates Structured Systems Analysis and Design Methodology (SSADM), iterative prototyping, and experimental validation. Arduino Uno and Nano boards, coupled with MPU6050 sensors, facilitate gesture detection. Signal processing algorithms were programmed in C++ on Arduino IDE. Performance results demonstrate system responsiveness within 100ms and operational wireless control over a 10?15 meter range, making it a viable educational and assistive robotics platform.},
      keywords = {Arduino, Gesture Recognition, Human-Robot Interaction, MPU6050.},
      month = {August},
  }