Carlos Noriega
Papers
2
Total Citations
19
H-Index
2
About
Carlos Noriega’s research sits at the vital intersection of human motor control and robotic rehabilitation, where understanding the body’s mechanics directly informs the design of smarter human-robot interfaces. His most cited work, “Elbow Joint Angle Estimation with Surface Electromyography Using Autoregressive Models” (2018, 15 citations), provides a critical method for decoding muscle signals into precise joint movements. By analyzing sEMG from the biceps, triceps, and brachioradialis, Noriega’s autoregressive model offers a non-invasive way to estimate elbow angles—a cornerstone for intuitive control of exoskeletons and prosthetics. This contribution not only advances interface design but also deepens our modeling of the musculoskeletal system. Complementing this technical work, his study on a “coincident timing motor task of the arm under a passive mechanical perturbation” (2014) explores how the brain learns and adapts to external forces. By experimentally assessing motor learning in perturbed environments, Noriega provides essential behavioral criteria for developing rehabilitation robots that work *with* human adaptation, not against it. Through these focused studies, Noriega demonstrates a clear commitment to bridging neural control theory with tangible engineering solutions for assistive technology.
Research Focus
Key Achievements
Top Papers
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