Miguel Xochicale

University of Birmingham, King's College London

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About

Miguel Xochicale is a researcher at the intersection of nonlinear dynamics, human-robot interaction, and surgical data science. His work centers on quantifying movement variability—a key to understanding sensorimotor control in both human-humanoid and clinical contexts. Xochicale’s foundational research, detailed in his 2019 dissertation (36 citations), applies nonlinear analysis to measure how humans imitate humanoid robots, using wearable inertial sensors to capture subtle variations in motion. This work laid the groundwork for objective, data-driven metrics in rehabilitation and robotics. More recently, he has advanced automated surgical skill assessment, developing real-time instrument tracking for endoscopic pituitary surgery (2024, 4 citations). By replacing subjective, labor-intensive evaluations with machine learning-driven metrics, his approach promises to improve training and patient outcomes. Xochicale’s contributions bridge theory and application, from quantifying imitation fidelity in healthy participants to building high-fidelity phantoms for surgical practice. His research is vital for students and researchers exploring how nonlinear dynamics and wearable sensing can transform human-robot collaboration and surgical education.