Catalina Alvarado‐Rojas
Papers
8
Total Citations
220
H-Index
5
About
Catalina Alvarado-Rojas is a biomedical engineer and researcher whose work sits at the compelling intersection of neural signal processing, machine learning, and robotic rehabilitation. Her research focuses primarily on electromyography (EMG)-based hand gesture recognition, exoskeleton control systems, and stroke motor recovery — areas where she has made meaningful contributions to both the science and clinical application of assistive technologies. Alvarado-Rojas's most influential work, "EMG-driven hand model based on the classification of individual finger movements" (2020, 94 citations), established a robust framework for translating muscle signals into precise finger motion commands — a critical step toward intuitive prosthetics and rehabilitation devices. Her subsequent studies on myoelectric pattern recognition (49 citations) and assist-as-needed exoskeleton systems (38 citations) demonstrate a sustained effort to bridge signal intelligence with real-world therapeutic hardware. What distinguishes her trajectory is the progressive sophistication of her systems: from adaptive trajectory generation to model predictive control-driven exoskeletons, and most recently, edge AI deployment and EEG-EMG fusion for movement intention decoding. With a growing citation record spanning over 220 citations, Alvarado-Rojas represents an emerging voice in the field of intelligent neurorehabilitation engineering.
Research Focus
Key Achievements
Top Papers
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- 2Myoelectric pattern recognition of hand motions for stroke rehabilitation49 citations · 2019
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