Aldi Sidik Permana
Papers
1
Total Citations
5
H-Index
1
About
Aldi Sidik Permana is a researcher focused on advancing human-robot interaction through intelligent gesture recognition and computer vision. His work centers on developing efficient, adaptive systems for interpreting hand movements, addressing critical challenges in real-world robotics applications. Permana’s most-cited paper, “Hand Movement Identification Using Single-Stream Spatial Convolutional Neural Networks” (2020), tackles two key issues: enabling robust performance in extreme or variable environments and optimizing memory usage by reducing the number of video frames processed. By proposing a streamlined CNN architecture, he contributes to making gesture-based control more practical and scalable for robotics, assistive technologies, and immersive interfaces. With 5 citations, this work lays groundwork for future studies in efficient human-machine communication. Permana’s research sits at the intersection of deep learning, spatial reasoning, and interactive systems, aiming to bridge the gap between complex neural models and real-time, resource-constrained deployment. His efforts are particularly valuable for students and engineers seeking to build responsive, low-latency robotic systems that can interpret natural human cues without overwhelming computational resources.
Research Focus
Key Achievements
Top Papers
- 1