Imdadullah Khan

Lahore University of Management Sciences

Papers

Total Citations

H-Index

About

Imdadullah Khan’s research focuses on biomedical signal processing, machine learning, and human–computer interaction, with a particular emphasis on electromyography (EMG)-based gesture recognition. His most-cited work, “Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal” (2020), systematically investigates how varying analysis window lengths and feature selection techniques influence the accuracy of classifying hand movements from surface EMG signals. This study provides practical guidance for designing more efficient and robust prosthetic control systems and human–machine interfaces. By demonstrating that careful tuning of temporal parameters and feature subsets can significantly improve classification performance, Khan’s work has contributed to advancing real-time, non-invasive control technologies. His findings are valuable for researchers developing assistive devices and rehabilitation technologies. With over 4 citations on this paper alone, his research continues to inform the design of smarter, more responsive systems that translate biological signals into intuitive commands. Khan’s work exemplifies the intersection of signal processing and applied machine learning, offering tangible improvements for medical and engineering applications.