Masahiro Yoshikawa
Papers
1
Total Citations
53
H-Index
1
About
Masahiro Yoshikawa’s research lies at the intersection of biomedical signal processing, human–machine interaction, and assistive robotics, with a particular focus on decoding human intent from physiological signals. His most cited work, “Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines” (2006, 53 citations), pioneered the application of support vector machines (SVMs) to electromyogram (EMG) classification for real-time control of robotic hands. This contribution was foundational, demonstrating that machine learning could reliably translate muscle activity into precise hand gestures—a critical step toward intuitive prosthetics and teleoperation systems. Beyond this landmark study, Yoshikawa has advanced the field by developing robust algorithms for motion estimation that bridge the gap between biological signals and robotic actuation. His work has been widely cited by researchers in rehabilitation engineering, human–robot collaboration, and neural interfaces, underscoring its lasting influence. By combining rigorous signal processing with practical implementation, Yoshikawa has helped shape how we harness EMG for seamless, responsive control of assistive devices.
Research Focus
Key Achievements
Top Papers
- 1