Doctoral research on explainable, leakage-free machine learning for the biomechanics of human locomotion — segmented normality patterns and biomechanical pre-diagnosis.
I've been a researcher with the UNLOC (Unlimited Locomotion) group at Universidad San Jorge since 2024, and a PhD candidate in Health Sciences since 2025. My research develops explainable AI and leakage-free machine-learning pipelines for clinical gait and foot-pressure data — turning complex biomechanical signals into transparent, trustworthy support for pre-diagnosis.
Segmented normality patterns and biomechanical pre-diagnosis.
Teaching computer engineering, artificial intelligence, and mobile technologies.
Academic direction of the master's programme.
A PRISMA-guided systematic review (registered on PROSPERO) mapping how explainable AI is applied across human-locomotion biomechanics.
A methodological manuscript on leakage-free per-pathology binary classification of locomotion data, with SHAP-based explainability of model decisions.
Making ML models transparent for clinical and sports professionals. SHAP for feature contributions, LIME for local interpretability, Grad-CAM for visual attribution, and counterfactuals for actionable "what-if" reasoning.
Analyzing clinical gait and foot-pressure data — baropodometry, optical motion capture, OptoGait, and inertial sensors — to characterize segmented normality patterns and support biomechanical pre-diagnosis.
Reproducible, leakage-free machine-learning pipelines and per-pathology binary classification, so that reported performance reflects true generalization rather than optimistic data leakage.
AI that enhances rather than replaces clinical judgment — interfaces and explanations designed around clinician and sports-science workflows, validated against clinical standards.
Clinicians and sports-science professionals are significantly more likely to act on AI recommendations when they understand the reasoning behind them.
Much of the reported accuracy in locomotion ML collapses under leakage-free validation. Honest pipelines matter more than headline metrics.
SHAP, LIME, Grad-CAM and counterfactuals reveal different facets of a decision. Combining them gives a fuller, more trustworthy explanation.
There is no single "normal" gait. Segmenting normality patterns is what makes biomechanical pre-diagnosis meaningful and fair.
My doctoral research on explainable AI and human locomotion has a natural applied home: sport. Through my innovation work with national and regional judo federations, the same methods — leakage-free pipelines, interpretable models, biomechanical pre-diagnosis — reach injury prevention, athlete monitoring, and the clinicians and coaches who support high performance.
The goal is the same in the lab and on the field: AI that the people behind human performance — sports scientists, physiotherapists, and the athletes themselves — can genuinely understand, question, and act on.
Open to collaborating on explainable AI, locomotion biomechanics, and applied research. Full academic record available on ORCID.