Fatan Kasyidi
Papers
1
Total Citations
5
H-Index
1
About
Fatan Kasyidi is a researcher specializing in human-robot interaction, computer vision, and deep learning, with a particular focus on gesture recognition systems. Their most notable contribution is the development of a single-stream spatial convolutional neural network for hand movement identification, a method that addresses critical challenges in real-time human-robot interaction. This work, published in 2020, tackles two persistent issues: adapting gesture recognition to extreme or varied environmental settings, and optimizing frame processing to reduce memory demands. Despite its technical sophistication, the paper has garnered 5 citations, reflecting its niche but growing influence in the field. Kasyidi’s research bridges the gap between robust machine learning models and practical robotic applications, offering solutions that enhance the fluidity and reliability of non-verbal human-machine communication. By prioritizing efficiency and adaptability, their work paves the way for more responsive and memory-conscious interaction systems, making it a valuable reference for students and researchers exploring gesture-based control in robotics.
Research Focus
Key Achievements
Top Papers
- 1