Luis Pelaez Murciego
Papers
2
Total Citations
23
H-Index
2
About
Luis Pelaez Murciego is a researcher at the forefront of human-machine interaction, specializing in myoelectric control and bio-inspired models for wearable robotics. His work centers on decoding motor intention from muscular activity to enable intuitive control of assistive and rehabilitative devices. In his most cited paper, "An undercomplete autoencoder to extract muscle synergies for motor intention detection" (2019, 18 citations), Pelaez Murciego pioneered the use of autoencoders to extract muscle synergies, offering a novel approach to myoelectric control that enhances natural and seamless interaction between humans and robots. This contribution addresses a critical challenge in wearable robotics: developing control strategies that are both intuitive and robust. His subsequent work, "Evaluating Generalization Capability of Bio-inspired Models for a Myoelectric Control: A Pilot Study" (2019, 5 citations), further explores the adaptability of these models across different users and conditions. Pelaez Murciego’s research has significant implications for the future of rehabilitation technologies, prosthetics, and assistive devices, bridging the gap between biological signals and machine interpretation. His innovative use of machine learning to decode complex neuromuscular patterns positions him as a key contributor to advancing human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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