Universidad de Valladolid · Valladolid, Spain

• Build computer vision pipelines to estimate human pose from monocular video for remote gait and posture assessment.
• Benchmark state-of-the-art deep learning models and integrate inertial sensor streams to improve kinematic accuracy.
• Collaborate with biomechanics researchers to translate experimental results into clinical-ready insights and tools.

MSc in Telecommunications Engineering

Universidad de Valladolid · 2025 - 2026 (in progress)

Graduate focus on advanced signal processing, computer vision, and applied AI for human-centred systems.

BSc in Telecommunication Technologies Engineering

Universidad de Valladolid · 2021 - 2025

Specialisation in embedded systems, PCB design, and machine learning. Awarded honours distinction for academic excellence.

Common Core Student

42 Paris · 2024

Intensive, project-based software engineering curriculum building strong foundations in algorithms and C programming.

Human Pose Detection Benchmark for Clinical Gait Analysis

Final Year Thesis · Universidad de Valladolid

Evaluated multiple convolutional and transformer-based detectors to measure human kinematics from video, proposing a reproducible pipeline that reduces evaluation time by 40% and improves accuracy on clinical datasets.

Multimodal Movement Assessment Toolkit

Research collaboration · 2024

Combined wearable inertial sensors with deep learning-based 3D pose estimators to deliver real-time feedback for rehabilitation specialists and sports scientists.

Comparative evaluation of muscular deep learning pose estimation and IMU-based systems for remote kinematic assessment

EMBC 2025 · IEEE Engineering in Medicine & Biology Society

Paving the way toward kinematic assessment using monocular video

Benchmarking state-of-the-art 3D human pose estimators with augmented inertial sensors for daily living scenarios.