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Electrooculography Based Control of a Robotic Manipulator with Dual Cameras for Object Retrieval Muhammad Ilhamdi Rusydi; Andre Paskah Gultom; Adam Jordan; Rahmad Novan Nurhadi; Darwison Darwison
International Journal of Basic and Applied Science Vol. 14 No. 4 (2026): March: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i4.798

Abstract

This study presents an assistive control system for a four-degree-of-freedom (4-DoF) robotic manipulator that integrates image-based spatial perception with electrooculography (EOG)-based human–machine interaction for three-dimensional object retrieval. The system is motivated by the need for intuitive, non-contact assistive technologies to support individuals with severe motor impairments, such as tetraplegia, in performing basic manipulation tasks. The proposed framework employs an orthogonal dual-camera vision configuration to achieve explicit 3D target localization, where planar object positions on the XY plane and depth along the Z axis are estimated using focal length–based geometric modeling. User commands are generated through an EOG interface, in which eye movements and voluntary blinks are classified using a K-Nearest Neighbor (KNN) algorithm to control manipulator motion. Compared to conventional assistive robotic systems that rely on depth sensors or high-degree-of-freedom manipulators, the proposed approach utilizes asymmetric monocular viewpoints and a minimal 4-DoF architecture to reduce system complexity. Experimental results demonstrate high performance, achieving average localization accuracies of 99.52% on the XY plane and 95.88% along the Z axis, as well as an EOG classification accuracy of 94.38%. Manipulation experiments confirmed reliable operation with a 100% task success rate, while task completion time and positional error increased gradually with target distance. These findings validate the feasibility of the proposed system as a low-complexity, high-accuracy assistive robotic solution for rehabilitation and human–machine interaction applications.
Co-Authors ., Bentar Abdul Rajab Abdul-Malek, Zulkarnain Abral, Hairul Adam Jordan Afwan Nasution Andi Pawawoi Andre Paskah Gultom Annita Elfia Rosa Ariadi Hazmi Arief Hadiwibowo Aulia . Bentar . Budi Rahmadya Budi Sunaryo Chatarina Umbul Wahyuni Deddi Prima Putra Defriyan Hidayat Dhanny Jufril Dilla Wediya Dwi Gustiono Eka Putra Waldi Ekaputra, Eri Gas Fadhila Amanda Fahranul Aderi Faisyal Razak Fajri, Rico Farhan Fadil Irsan Fauzan Wafi Fauzan Wafi Firman Ridwan Ghina Salsabila Hermawan Nugroho Ilhamdi Rusydi Irsyad Alghi Fary Laksono, Heru Dibyo M. A. Hafizi M. H. Ahmad M. Heru Setiawan M. Ilhamdi Rusydi Maemonah, Maemonah Melda Latif Minarni Minarni Mondrizal Mondrizal Mumuh Muharram Novizon, Novizon Rahmad Novan Nurhadi Rahmadi Kurnia Ratna Aisuwarya Razak, Faisyal Reivan Arya Witama Reni Desmiarti Reni Desmiarti Rian Wahyudi Rico Fajri Riko Nofendra Rudy Fernandez Rusydi, Ilhamdi Rusydi, M. Ilhamdi Rusydi, Muhammad Ilhamdi Silvia Permata Sari Surya Afnarius Syarkawi Syamsuddin Syukri Arief Syukri Yunus Syukur, Masril Tesya Uldira Septiyeni Tia Priska Sari Wahyudi, Rian Zaini Zaini Zaini Zaini Zaini Zaini Zaini Zaini Zaini Zaini Zaini, Zaini Zulkarnain Abdul-Malek