Bushra Saeed

National University of Sciences and Technology

Papers

Total Citations

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About

Bushra Saeed’s research lies at the intersection of biomedical signal processing and machine learning, with a particular focus on electromyographic (EMG) signals for assistive technologies. Her most cited work, “Comparative Analysis of Classifiers for EMG Signals” (2019), systematically evaluates machine learning algorithms for pattern recognition of arm movements, directly addressing the needs of individuals with upper limb impairments or paralysis. By identifying optimal classifiers for robotic hand prostheses, Saeed’s contributions help bridge the gap between raw biological signals and practical, real-time control systems in biomedical applications. With 8 citations, this study has informed subsequent work in prosthetic design and rehabilitation engineering. Saeed’s research demonstrates a clear commitment to translating computational methods into tangible solutions that improve quality of life for disabled individuals. Her work is especially valuable for students and researchers exploring how signal classification can enhance human-machine interfaces, offering a foundational reference for those entering the field of biomedical signal analysis and assistive robotics.