Shota Masuda
Papers
1
Total Citations
3
H-Index
1
About
Shota Masuda is a researcher in human-robot interaction and gesture-based control systems, with a focus on intuitive interfaces that bridge the gap between human motion and machine response. His most cited work, "Face direction recognition system for robot control system using fingertip gesture" (2017, 3 citations), introduces a novel method for integrating facial orientation cues—specifically yaw-angle detection—with fingertip gestures to enhance robot command accuracy. By leveraging the Intel RealSense SR300 depth sensor, Masuda developed a system that recognizes three distinct face directions (front, rightward, leftward) as supplementary inputs, enabling more natural and robust control in real-time environments. This contribution addresses key challenges in non-verbal human-robot communication, particularly for users with limited mobility or in noisy settings where voice commands fail. While his citation count reflects a focused, emerging impact, Masuda's work stands out for its practical integration of low-cost, off-the-shelf sensors into accessible robotic interfaces. His research underscores a commitment to user-centered design, offering a foundation for future advancements in assistive robotics and gesture-driven automation.
Research Focus
Key Achievements
Top Papers
- 1