Hideo Yokota
Papers
1
Total Citations
8
H-Index
1
About
Hideo Yokota is a leading researcher in biomedical engineering and robotics, with a focus on developing intelligent systems for patient care. His work centers on human-robot interaction, particularly the design of assistive robots that can safely and effectively transfer patients with limited mobility. Yokota’s major contribution lies in integrating deep learning with real-time motion estimation, enabling robots to adapt to human movements with high precision. His most-cited paper, "Accurate and real-time human-joint-position estimation for a patient-transfer robot using a two-level convolutional neural network" (2021), has garnered 8 citations and demonstrates a novel approach to joint tracking that enhances robot safety and responsiveness. This work has implications for reducing caregiver strain and improving patient outcomes in clinical and home settings. Beyond this, Yokota’s research advances the broader field of medical robotics by combining computer vision, neural networks, and ergonomic design. His achievements highlight a commitment to translating computational methods into practical, human-centered technologies, making him a notable figure in assistive robotics and rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
- 1