Rihito Kanazawa
Papers
1
Total Citations
6
H-Index
1
About
Rihito Kanazawa is a researcher at the forefront of biomechatronics and human-robot interaction, with a focus on developing intuitive control systems for wearable robotic exoskeletons. His key research areas include mechanomyography (MMG)-based torque estimation, biomechanical modeling, and assistive robotics. Kanazawa’s major contribution is his pioneering work on real-time elbow-joint torque estimation using MMG signals—a non-invasive method that captures muscle vibrations to infer muscle activity. In his most-cited paper (2018, 6 citations), he proposed a simplified musculotendinous model that integrates MMG signals to estimate joint torque, enabling more responsive and natural assistive control for exoskeletons. This approach addresses a critical challenge in wearable robotics: acquiring direct, real-time muscle activity without the limitations of electromyography (EMG). While his citation count is modest, his work represents a foundational step toward seamless human-machine collaboration, particularly for rehabilitation and assistive devices. Kanazawa’s research holds promise for advancing adaptive exoskeletons that can intuitively support users in daily tasks or recovery, marking him as an emerging voice in the field of human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1