Nonlinear Analysis to Quantify Movement Variability in Human-Humanoid Interaction

Abstract

Nonlinear analysis can be applied to investigate the dynamics of time-ordered data. Such dynamics relate to sensorimotor variability in the context of human-humanoid interaction. Hence, this dissertation not only explores questions such as how to quantify movement variability or which methods of nonlinear analysis are appropriate to quantify movement variability but also how methods of nonlinear analysis are affected by real-world time series data (e.g. non-stationary, data length size, sensor sources or noise). Methods are explored to determine embedding parameters, reconstructed state spaces, recurrence plots and recurrence quantification analysis. Additionally, this thesis presents three dimensional surface plots of recurrence quantification analysis with which to consider the variation of embedded parameters and recurrence thresholds. These show that three dimensional surface plots of Shannon entropy might be a suitable approach to understand the dynamics of real-world time series data. This thesis opens new avenues of applications in human-humanoid interaction where humanoid robots can be pre-programmed with nonlinear analysis algorithms to evaluate, for instance, the improvement of movement performances, to quantify and provide feedback of skill learning or to quantify movement adaptations and pathologies.

Content

Individual chapters and full document of this PhD thesis are available in the following zenodo links.

Chapter 1. Introduction
Chapter 2. Quantifying Movement Variability
Chapter 3. Nonlinear Analysis
Chapter 4. Experiments
Chapter 5. Quantifying Human-Image Imitation Activities
Chapter 6. Quantifying Human-Humanoid Imitation Activities
Chapter 7. Conclusions and future work
Appendixes
References
FULL PHD THESIS

Open access PhD Thesis

This repository contains the source code and data to make this work reproducible and perhaps help others to advance this field. Throughout the thesis links to R code and data are provided in the caption of figures. See code data path for details on how code and data is organised and how results can be replicated.

PhD thesis milestones

• On 26 October 2018, I submitted my PhD thesis and such version is available here: .
• On 11 Junuary 2019, I had my PhD viva and I passed with major corrections (see here for the preparation of my PhD viva).
• Then, on 20 May 2019 I completed the major corrections and I submitted the corrected thesis which is available here.
• On 18 June 2019, my PhD examiners certificated that major corrections have been carried out to their satisfaction. That said, you can download here the release version of the thesis for the printing and binding.
• Finally, on 30 August 2019, I relased the final version of my PhD thesis.

Licence and Citation

The work of this thesis 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 PhD thesis as original reference and you re-share your work under the same terms.

Cite as

Xochicale M. (2019). Nonlinear Analysis to Quantify Movement Variability in Human-Humanoid Interaction (PhD thesis). Retrieved from https://doi.org/10.5281/zenodo.3384145

BibTeX to cite

@phdthesis{XochicalePhDThesis2019,
	author = {Xochicale Miguel},
	day = {30},
	month = {08},
	Year = {2019},
	school = {University of Birmingham},
	address = {Birmingham, United Kingdom},
	Title = {Nonlinear Analysis to Quantify Movement Variability in Human-Humanoid Interaction},
	type = {{PhD} thesis},
	doi = {10.5281/zenodo.3384145},
	url = {https://doi.org/10.5281/zenodo.3384145}
}

Contact

If you have specific questions about the content of this thesis, you can contact Miguel Xochicale. If your question might be relevant to other people, please instead open an issue.