Eko Mulyanto Yuniarno
Papers
7
Total Citations
45
H-Index
4
About
Eko Mulyanto Yuniarno is a leading researcher in robotics and computer vision, with a focus on human-robot interaction, pose estimation, and autonomous systems. His work spans mobile robotics, multi-agent coordination, and assistive technologies for elderly care. Notably, his most cited paper, "Pembuatan Modul Deteksi Objek Manusia Menggunakan Metode YOLO untuk Mobile Robot" (23 citations), demonstrates his expertise in integrating deep learning for real-time human detection in mobile robots. Yuniarno has made significant contributions to inverse kinematics for humanoid robots, including the open-source ARKOMA dataset (2023), which enables neural network-based modeling for NAO robot arms. His research on cooperative multi-agent systems (6 citations) and resource-efficient robot services for the elderly (5 citations) highlights his commitment to practical, socially impactful applications. Yuniarno has also advanced human pose detection with models like EELAN-Blazepose and datasets such as HiroPoseEstimation, while exploring 3D body orientation classification using modified CNN architectures. With a growing citation record and a focus on low-computational devices, his work bridges cutting-edge AI with accessible robotics, making him a key figure in developing intelligent, collaborative systems for real-world challenges.
Research Focus
Key Achievements
Top Papers
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- 5Multi-Human Pose Detection Based on EELAN-Blazepose Model2 citations · 2023
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