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About

Mohamed Abdelhady is a researcher at the intersection of digital health, biomedical engineering, and assistive robotics, whose work spans crisis management technologies to neural-controlled prosthetics. His most cited paper, "The role of contemporary digital tools and technologies in COVID‐19 crisis: An exploratory analysis" (45 citations), examines how digital resources—from tracking systems to data analytics—were deployed to contain the pandemic, offering critical insights for future public health emergencies. In the field of wearable robotics, Abdelhady has made significant contributions to movement intention estimation, particularly through his pioneering work on knee angle estimation from surface electromyography (sEMG) signals. His 2024 study, "Knee Angle Estimation from Surface EMG during Walking Using Attention-Based Deep Recurrent Neural Networks," demonstrated a novel attention-based deep learning approach that overcomes individual variability, achieving accurate joint angle prediction for individuals with cerebral palsy. This work, building on his earlier 2023 paper (3 citations), holds promise for more intuitive control of exoskeletons and prosthetics. Additionally, his 2017 paper on robotics and prosthetics at Cleveland State University (16 citations) highlights his foundational contributions to modeling and communication technologies in assistive devices. With a growing citation record and a focus on translating neural signals into practical robotic control, Abdelhady is advancing the frontier of human-machine interaction for rehabilitation.