mxochicale / seminario-cicese-27112020

Nonlinear analysis to quantify human movement variability from time-series data

Abstract:

In this talk I will speak about (a) theoretical models for human movement variability and methods to quantify human movement variability, (b) use of nonlinear methods to measure real-world time series data (i.e., data affected by non-stationarity, non-linearity, data length, sensor source, noise, etc.), and (c) illustration of results for real-world time-series data from human-robot imitation activities. I will then comment on current and future challenges on this subject and how the above points might lead to develop tools to evaluate, for instance, the improvement of movement performances, to quantify and provide feedback of skill learning or to quantify movement adaptations and pathologies. Similarly, I will speak about my current role in a multidisciplinary project in the context of Ultrasound-Guidance Procedures where I am jumping between the areas of medical imaging, AI, physics and robotics .

Short Bio:

Miguel Xochicale is currently a Research Associate at King's College London within the School of Biomedical Engineering and Imaging Sciences where he is pushing forward the state-of-the-art of ultrasound-guidance procedures and is making scientific contributions to new algorithms, software and hardware. Prior to that, he was awarded a Ph.D. degree in Computer Engineering from the University of Birmingham (UoB) in July 2019 where he investigated Nonlinear Analysis to Quantify Movement Variability in Human-Humanoid Interaction and published the first open-accessed and reproducible PhD thesis since the establishment of UoB in 1900. Miguel has 20 years’ experience in human-robot interaction, electronics, mechatronics and signal processing, along with 12 years’ experience as a teaching associate in Mechatronic and Computer Engineering. He has passion for Open Science meaning that he open-sourced all his contributions to knowledge in GitHub. He also tweets and re-tweets about Robotics, Chaos, AI and Brains @_mxochicale

Licence and Citation

This work is under Creative Commons Attribution-Share Alike license . Hence, you are free to reuse it and modify it as much as you want and as long as you cite this work as original reference and you re-share your work under the same terms.

Bibtex

@misc{miguel_xochicale_2020_4293750,
  author       = {Miguel Xochicale},
  title        = {{Nonlinear analysis to quantify human movement 
                   variability from time-series data (\& my research)}},
  month        = nov,
  year         = 2020,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4293750},
  url          = {https://doi.org/10.5281/zenodo.4293750}
}

Contact

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