Jhih-Wei Jian
Papers
1
Total Citations
2
H-Index
1
About
Jhih-Wei Jian is a researcher whose work sits at the intersection of human-robot interaction and machine learning, with a particular focus on intuitive control systems. His most cited paper, "Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method" (2013, 2 citations), introduces a novel approach to robot teleoperation by leveraging the Kinect sensor's depth-sensing capabilities. Jian's key contribution lies in designing a system that captures human body skeleton data and employs machine learning to recognize gestures, enabling operators to control mobile robots through natural body movements rather than traditional joysticks or keyboards. This work demonstrates his commitment to making robotic control more accessible and user-friendly. While his citation count is modest, the research represents an early and innovative application of consumer-grade depth sensors—like the Kinect—to robotics, anticipating later developments in gesture-based control systems. Jian's approach of combining affordable hardware with machine learning for practical robotics applications highlights his focus on creating simple, convenient, yet effective solutions that bridge the gap between human intention and machine action.
Research Focus
Key Achievements
Top Papers
- 1