Omar Coser
Papers
2
Total Citations
78
H-Index
2
About
Omar Coser is at the forefront of integrating artificial intelligence with wearable robotics, specializing in AI-driven control systems for lower-limb exoskeletons used in rehabilitation and human locomotion analysis. His major contributions center on developing and comparing deep learning methodologies that enable exoskeletons to adapt intelligently to individual users, moving beyond rigid, pre-programmed assistance. His highly cited 2024 review, "AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb," (73 citations) established a foundational framework for the field, systematically mapping how machine learning can personalize therapy for individuals with lower-limb impairments. Building on this, his 2025 comparative study on deep learning for human locomotion analysis (5 citations) provides critical benchmarks for real-time, adaptive control, addressing a core challenge in making exoskeletons responsive and safe. Coser’s work is notable for bridging the gap between theoretical AI models and practical, clinically viable assistive devices, directly impacting the design of next-generation rehabilitation technologies. His research is essential reading for students and engineers working at the intersection of robotics, rehabilitation science, and artificial intelligence.
Research Focus
Key Achievements
Top Papers
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