Muhammad Mutoha
Papers
1
Total Citations
27
H-Index
1
About
Muhammad Mutoha is a researcher in assistive robotics and human-machine interaction, with a focus on wearable robotic systems that enhance human capabilities. His most cited work, "Grasp Posture Control of Wearable Extra Robotic Fingers with Flex Sensors Based on Neural Network" (2020, 27 citations), introduces a novel data-driven approach for controlling extra robotic fingers that assist users in bimanual object manipulation. The system features a robotic thumb attached next to the user’s thumb and robotic fingers that work in coordination, using flex sensors and neural networks to interpret grasp postures. This contribution is significant for developing intuitive, adaptive control methods that allow users to perform tasks requiring two hands with minimal cognitive load. Mutoha’s research addresses key challenges in wearable robotics, such as seamless integration with natural human movement and real-time sensorimotor feedback. His work has implications for rehabilitation, industrial assistance, and augmenting human dexterity, offering practical solutions for individuals with limited hand function or those needing extra support in complex tasks. By combining neural network-based control with wearable design, Mutoha advances the field toward more responsive and user-friendly robotic aids.
Research Focus
Key Achievements
Top Papers
- 1