Papers
3
Total Citations
29
H-Index
3
About
Anubha Parashar is a researcher whose work sits at the intersection of human-computer interaction, assistive technology, and robotics. Her key research areas include hand gesture recognition, humanoid robotics, and machine learning applications for healthcare and communication. Parashar’s most notable contribution is her 2024 deep learning framework for hand gesture recognition, which targets critical applications in deaf communication and healthcare—a paper that has already garnered 12 citations, signaling its growing influence in the field. In earlier work, she tackled the challenge of bipedal stability in humanoid robots, developing push recovery control systems and using K-means clustering to classify dynamic environmental data. Her 2016 paper on this topic has accumulated 11 and 6 citations across its versions, demonstrating sustained interest in her approach to robust robot locomotion. Parashar’s research is distinguished by its practical orientation: she bridges advanced AI techniques with real-world needs, from enabling seamless human-computer interaction for the hearing impaired to engineering more resilient humanoid platforms. Her work is particularly valuable for students and researchers exploring how machine learning can make technology more inclusive and physically capable.
Research Focus
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