Surender Hans

Malaviya National Institute of Technology Jaipur

Papers

Total Citations

H-Index

About

Surender Hans is a researcher at the forefront of human-machine interaction, specializing in surface electromyography (sEMG) signal processing and deep learning for gesture recognition. His work focuses on developing efficient, automated neural network architectures that can interpret muscle activity for prosthetic control and wearable technology. Hans’s most notable contribution is his pioneering application of 1D convolutional neural architecture search (NAS) to sEMG-based hand gesture recognition, a method that automates the design of optimal deep learning models for real-time, high-accuracy classification. This approach addresses a critical bottleneck in the field—manually designing networks that balance computational efficiency with recognition performance. While his 2025 paper is early in its citation lifecycle, its innovative methodology promises to accelerate progress in assistive robotics and rehabilitation engineering. Hans’s research is particularly impactful for students and engineers seeking to bridge the gap between biological signals and intelligent systems, offering a scalable framework that reduces reliance on handcrafted features. His work stands as a testament to the growing synergy between automated machine learning and biomedical applications, with potential to transform how amputees and individuals with motor impairments interact with their environment.