R. A. Samudro
Papers
1
Total Citations
2
H-Index
1
About
R. A. Samudro is a researcher in assistive robotics and human-machine interaction, with a focus on developing intelligent systems to improve the quality of life for individuals with physical disabilities. Their key research areas include neural network applications, extreme learning machines, and robotic control systems for assistive technologies. Samudro’s most notable contribution is the comparative study of Extreme Learning Machine (ELM) and Backpropagation Neural Network (BPNN) methods for controlling a hand typist robot designed for quadriplegic individuals. This work, published in 2017, critically evaluates prediction accuracy between the two approaches, highlighting the importance of method selection in optimizing robotic performance. Although the paper has garnered 2 citations, its significance lies in addressing a crucial gap in assistive robotics: ensuring reliable, error-minimized control systems for users with severe motor impairments. By demonstrating the trade-offs between ELM and BPNN, Samudro’s research provides foundational insights for developing more responsive and accurate robotic aids. This work underscores their commitment to bridging computational intelligence and practical rehabilitation engineering, offering a pathway toward more autonomous and empowering technologies for quadriplegic individuals.
Research Focus
Key Achievements
Top Papers
- 1