Ulises Daniel Serratos Hernandez

Ulises Daniel Serratos Hernandez

Human Movement | Data Science | Researcher

Wellcome Centre for Human Neuroimaging

Profile

Human movement and data science researcher with expertise in machine learning, action classification, motion tracking, and human motion modelling. My doctoral research established a novel framework for quantifying upper limb dexterity, integrating manipulability, workspace, and range of motion analysis. Currently, I apply immersive virtual reality and advanced machine learning techniques to dissect the fundamental components of human threat avoidance behaviour. My work involves developing an unsupervised clustering model to identify fundamental movement motifs (“syllables”) and sequences (“grammar”) to create a comprehensive dictionary of threat avoidance actions, as well as a robust classification system. This research encompasses motion capture (marker-based and marker-less), data curation, feature engineering, dimensionality reduction, and model implementation and evaluation in Python (scikit-learn). My overarching goal is to leverage artificial intelligence to understand complex human movement patterns and address real-world challenges.

Interests
  • Data Science
  • Machine Learning
  • Human Movement Sciecne
  • Action Classification
  • Biomechanics
Education
  • Doctor of Philosophy in Mechanical Engineering, 2021

    The University of Sheffield

  • Master of Engineering, 2011

    Universidad La Salle Bajio

  • BEng in Mechanical and Electrical Engineering, 2009

    Universidad La Salle Bajio

Skills

Coding

Python, MATLAB, C#, Version control (GitHub, Gitlab, Bitbucket)

Machine Learning

Supervised and unsupervised (e.g., decision trees, random forest, regression, bayes, density-based, hierarchical, and k-means)

ML Libraries

Scikit-learn, Pandas, NumPy, Matplotlib

Movement Science

Marker-based (e.g., Vicon Motion Systems, CODA motion), and marker-less (Openpose, DeepLabCut, OpenCV), Action Classification, Motion Modelling

VR

Unity, HTC Vive, Oculus Quest

Languages

Spanish (Native), English, German (Basic-Intermediate)

Experience

 
 
 
 
 
My work involves developing an unsupervised clustering model to identify fundamental movement motifs (“syllables”) and sequences (“grammar”) to create a comprehensive dictionary of threat avoidance actions, as well as a robust classification system.
 
 
 
 
 
The University of Sheffield
Doctor of Phylosofy Candidate
October 2016 – October 2020 UK
My doctoral research established a novel framework for quantifying upper limb dexterity, integrating manipulability, workspace, and range of motion analysis.
 
 
 
 
 
The University of Sheffield
Graduate Teaching Assistant
February 2017 – February 2020 UK
Graduate Teaching Assistant (Deparment of Mechanical Engineering) for the modules MATLAB, Design Innovation Toolbox, Integrated Design Skills, Strategic Engineering Management and Business Practices, Danger Lab.
 
 
 
 
 
INSIGNEO, Department of Mechanical Engineering, The University of Sheffield
Principal Investigator (INSIGNEO Summer Research Programme)
July 2019 – November 2019 UK
Led a 12-week Insigneo Summer Research Programme project at the University of Sheffield. Designed research objectives, experimental protocols, and secured ethics approval. Supervised and mentored an undergraduate student, fostering their interest in the field of in silico medicine.

Recent Publications

(2023). Biomechanical constraints on escape from threat in virtual reality: Preliminary findings. Gait & Posture.

Cite DOI

(2023). Movement tracking and action classification for human behaviour under threat in virtual reality. Gait & Posture.

Cite DOI

(2020). Computational Characteristics of Human Escape Decisions. PsyArXiv.

Cite

(2019). Upper limb manipulabilty analysis and uncertainty propagation. 25th Congress of the European Society of Biomechanics.

Cite

Contact

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