Omid Bahador

Islamic Azad University South Tehran Branch

Papers

Total Citations

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About

Omid Bahador is a researcher specializing in the intersection of artificial intelligence and biomedical engineering, with a primary focus on gesture recognition using surface electromyography (sEMG) signals. His most cited work, "High accurate lightweight deep learning method for gesture recognition based on surface electromyography" (2020), has garnered 29 citations, demonstrating its influence in advancing efficient, real-time human-machine interfaces. Bahador’s key contributions lie in developing deep learning architectures that balance high accuracy with computational lightness, enabling practical deployment of sEMG-based systems for prosthetic control, rehabilitation, and interactive technologies. By optimizing neural networks for resource-constrained environments, his research addresses critical challenges in wearable and assistive devices, making gesture recognition more accessible and responsive. Bahador’s work is notable for its emphasis on bridging algorithmic innovation with real-world applicability, offering scalable solutions that reduce latency and power consumption without sacrificing performance. His findings have implications for both clinical and consumer applications, from improving amputee mobility to enhancing virtual reality interactions. As a researcher, Bahador continues to push the boundaries of lightweight AI, contributing to a future where intuitive, bio-signal-driven interfaces become seamlessly integrated into daily life.