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About

Luciano Boquete is a researcher whose work sits at the compelling intersection of biomedical engineering, human-computer interaction, and assistive robotics. His most significant contributions center on the development of eye-controlled systems for mobility assistance, particularly wheelchair guidance technologies designed to empower individuals with severe motor disabilities. Boquete has pioneered the application of electro-oculography (EOG) — a technique that measures the electrical potential produced by eye movements — as a reliable and non-invasive control interface for robotic wheelchairs. His 2003 paper on EOG-based wheelchair guidance, which has accumulated 76 citations, introduced an innovative method using eye movement codification and inverse eye modeling to translate saccadic movements into precise navigational commands. Building on this, his 2002 work demonstrated that neural networks could further refine EOG-based control systems, earning 64 citations and establishing him as a leading voice in neural-network-driven assistive technology. Boquete has continued extending this framework through recurrent neural network architectures, reflecting a consistent commitment to advancing intelligent, adaptive control systems. His body of work represents a meaningful contribution to accessible technology, offering practical pathways toward greater independence for people with physical disabilities.