Meet Kumari
Papers
1
Total Citations
3
H-Index
1
About
Kumari is an emerging researcher in biomedical signal processing and rehabilitation robotics, with a focused interest in harnessing surface electromyography (SEMG) signals for advanced human-machine interfaces. Her most-cited work, "SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features" (2022), demonstrates her early contributions to developing robust classification methods—specifically decision trees and logistic regression—for identifying muscle activity patterns. This research, which has garnered 3 citations, addresses a critical challenge in the field: translating raw biological signals into reliable commands for robotic prosthetics and assistive devices. By leveraging wavelet-based feature extraction, Kumari’s approach enhances the accuracy and efficiency of SEMG signal interpretation, paving the way for more intuitive and responsive rehabilitation technologies. Her work sits at the intersection of signal processing, machine learning, and robotics, reflecting a commitment to creating practical solutions that improve quality of life. As an early-career researcher, Kumari’s foundational studies signal a promising trajectory in the development of non-invasive, biologically-driven control systems for next-generation assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1