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All Journal Buletin Ilmiah Sarjana Teknik Elektro International Journal of Basic and Applied Science
Nurhadi, Rahmad Novan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics

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Electrooculography and Camera-Based Control of a Four-Joint Robotic Arm for Assistive Tasks Rusydi, Muhammad Ilhamdi; Gultom, Andre Paskah; Jordan, Adam; Nurhadi, Rahmad Novan; Windasari, Noverika; Sasaki, Minoru; Ramlee, Ridza Azri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14305

Abstract

Individuals with severe motor impairments face challenges in performing daily manipulation tasks independently. Existing assistive robotic systems show limited accuracy (typically 85–92%) and low intuitive control, requiring extensive training. This study presents a control system integrating electrooculography (EOG) signals with real-time computer vision feedback for natural, high-precision control of a 4-degrees-of-freedom (4-DOF) robotic manipulator in assistive applications. The system uses an optimized K-Nearest Neighbors (KNN) algorithm to classify six eye-movement categories with computational efficiency and real-time performance. Computer-vision modules map object coordinates and provide feedback integrated with inverse kinematics for positioning. Validation with 10 able-bodied participants (aged 18–22) employed standardized protocols under controlled laboratory conditions. The KNN classifier achieved 98.17% accuracy, 98.47% true-positive and 1.53% false-negative rates. Distance-measurement error averaged 1.5 mm (± 1.6 mm). Inverse-kinematics positioning attained sub-millimeter precision with 0.64 mm mean absolute error (MAE) for frontal retrieval and 1.58 mm for overhead retrieval. Operational success rates reached 99.48% for frontal and 97.96% for top-down retrieval tasks. The system successfully completed object detection, retrieval, transport, and placement across ten locations. These findings indicate a significant advancement in EOG-based assistive robotics, achieving higher accuracy than conventional systems while maintaining intuitive user control. The integration shows promising potential for rehabilitation centers and assistive environments, though further validation under diverse conditions, including latency and fatigue, is needed.
Electrooculography Based Control of a Robotic Manipulator with Dual Cameras for Object Retrieval Rusydi, Muhammad Ilhamdi; Gultom, Andre Paskah; Jordan, Adam; Nurhadi, Rahmad Novan; 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 Darwison Darwison Gultom, Andre Paskah Jordan, Adam Muhammad Rusydi H. Noverika Windasari Ramlee, Ridza Azri Sasaki, Minoru