submission

README.md

@@ -1,6 +1,8 @@
 PhD Thesis
 ---
-My PhD Thesis as submitted on 26th October 2018. 
+This repository contains my PhD thesis as submitted on 26th October 2018.
+Code and data for its replication is available [here](https://github.com/mxochicale/phd-thesis-code-data).
+
 
 ## Citation
 If you use or adapt any of the files in this repository,
@@ -16,17 +18,23 @@ use the following BibTeX to cite [my PhD thesis](https://github.com/mxochicale/p
   	address = {Birmingham, United Kingdom},
     	Title = {Nonlinear Analyses to Quantify Movement Variability in Human-Humanoid Interaction},
   	type = {{PhD} dissertation as submitted},
+ 	note = {As submitted, awaiting viva and further revision},
+	doi = {10.5281/zenodo.1473140},
+        url = {https://doi.org/10.5281/zenodo.1473140}
 }
 ```
 
->> **NB** I will be polishing and updating this repository
-while I am wait for my viva (tentatively January 2019 or February 2019).
 
 
-# Issues
-If you see any errors or have any questions
-please create an [issue](https://github.com/mxochicale/phd-thesis/issues)
+# Notes
+> I will be polishing and updating this repository while I am awaiting viva 
+(tentatively for viva might be in January 2019 or February 2019).
+
+
 
+# Contact 
+If you have specific questions about the content of this thesis, you can contact [Miguel Xochicale](mailto:[email protected]?subject="[PhD thesis]").
+If your question might be relevant to other people, please instead [open an issue](https://github.com/mxochicale/phd-thesis/issues).
 
 
 

abstract/abstract.tex

@@ -5,118 +5,29 @@
 
 %%%% 200 words %%%%
 \begin{abstract}
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%% One or two sentences proving a basic introduction to the field,
-%%%%% comprehensible to a scientist in any discipline.
 Nonlinear analyses investigate the dynamics of observed time-ordered data.
 Such dynamics, for this thesis, are complex systems of sensorimotor 
 variables of movement variability (MV) in the context of 
 human-humanoid interaction.
-%31 words
-%movement variability (MV) and also in the mechanical 
-%limitations of a humanoid robot (robot with the general structure of a human).
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%% Two to three sentences of more detailed background,
-%%%%% comprehensible to scientist in related disciplines.
 Hence, this dissertation not only explores questions such as what to quantify in MV?,
 or which nonlinear tools are appropriate to quantify MV?, but also how 
 nonlinear analyses are affected with real-world time series data
 (e.g. nonstationary, data length limitations, sampling rate changes or noisiness).
-%46 words
-%We conducted two experiment in human-humanoid interaction where participants 
-%imitate simple horizontal and vertical arm movements of a humanoid robot.
-%to test the 
-%Given that traditional methods in time-domain and frequency domain fail to
-%detect tiny modulations in frequency or phase of time series,
-%we consider a methodology from nonlinear dynamics called uniform reconstructed state space 
-%to quantify movement variability which essentially the dynamics of
-%an unknown system can be reconstructed using one dimensional time series.
-%As pointed out by Bradley et al. uniform reconstructed state space,
-%if done right, can guarantee to be topologically identical to the true dynamics
-%and determine dynamics invariants such as fractal dimension, Kolmogorov-Sinai
-%entropy or Lyaponov exponents.
-%These algorithms, however, require time series measured with costly sensors
-%that provide well sampled data with little noise.
-%Such requirement is generally a common problem when doing precise
-%characterisation of time series using dynamic invariants,
-%to which Bradley et al. proposed additional tools of
-%nonlinear time series analysis for practitioners such as surrogate data,
-%permutation entropy, recurrence plots and 
-%network characteristics for time series
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% One sentence clearly stating the general problem being addressed by this
-%%%%%% particular study.
 Particularly, I review nonlinear tools such as methods 
 to determine embedding parameters, Reconstructed State Spaces (RSSs), 
 Recurrence Plots (RPs) and Recurrence Quantification Analyses (RQA).
-%25 words
-%(window size length) and 
-%preprocessing techniques (e.g. smoothing and normalisation) 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% One sentence summarising the main result (with the words "here we show"
-%%%%%% or their equivalent)
-%such as window size length, participants, sensors and levels of smoothness
-%(see weaknesses and strengths of RQA in Chapters \ref{chapter5} and 
-%\ref{chapter6}).
-%So, here we show the characterisation for time series to understand 
-%human movement variability in the
-%context of human-humanoid imitation activities and demonstrate the potential 
-%of nonlinear techniques to quantify human movement variability. 
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% Two or three sentences explaining what the main results reveals in direct
-%%%%%% comparison to what was thought to be the case previously, or how the main
-%%%%%% results adds to previous knowledge.
 To my knowledge, I can conclude that, no scientific work has been reported 
 regarding nonlinear analyses (e.g. RSSs with UTDE, RPs and RQAs) to 
 quantify movement variability in the context of human-humanoid interaction.
-%34 words
-%thorough experimentation and exploration to test 
-%the weaknesses and robustness of such tools, 
 Also, we created 3D surfaces of RQA
 values considering the variation of embedded parameters and 
 recurrence thresholds to show that 3D surfaces of RQA ENTR might be a 
 better approach to provide understanding on the dynamics of different 
 characteristic of real-world time series data. 
-%44 words
-%Specifically, we explore the reconstruction of state spaces, its recurrence plots
-%and metrics of recurrence quantification analysis for
-%20 participants performing repetitions of simple vertical and horizontal arm
-%movements in normal and faster speed.
-%We also explore the differences between wearable inertial sensors attached
-%to the person and to the humanoid robot and between different axes of inertial sensors.
-%With that in mind, our contribution to knowledge is in regard to the
-%reliability of data from cheap wearable inertial sensors
-%to analyse human movement variability in the context of human-humanoid imitation activities
-%using methodologies of nonlinear dynamics.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% One or two sentences to put the results into a more general context.
-%Such understanding and measurement of movement variability using
-%cheap wearable inertial sensors lead us to have a more intuitive selection of parameters
-%to reconstruct the state spaces and to create meaningful interpretations
-%of the recurrence plots and the results of the metrics with recurrence quantification 
-%analysis. Additionally, having a better understanding of
-%nonlinear dynamics tools with the use of cheap inertial sensors
-%can enhance the development of better diagnostic tools for various pathologies 
-%which can be applied in areas of rehabilitation, entertainment or sport science.
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%%% Two or three sentences to provide a broader perspective, 
-%%%%%% readily comprehensible to a scientist in any discipline, 
-%%%%%% may be included in the first paragraph
-%%%%%% if the editor considers that the accessibility of the paper is 
-%%%%%% significantly enhanced by their inclusion. Under this circumstances, 
-%%%%%% the length of the paragraph can be up to 300 words
 I can foresee many areas of applications where humanoids robots
 can be pre-programmed with nonlinear analyses algorithms 
 to evaluate the improvement of movement performances,
 to quantify and provide feedback of skill learning
 or to quantify movement adaptations and pathologies.
-%38 words
-
-
-
 
 \end{abstract}

acknowledgement/acknowledgement.tex

@@ -1,7 +1,6 @@
 % ************************** Thesis Acknowledgements **************************
 
 \begin{acknowledgements}      
-
 I would like to acknowledge to the Mexican National Council of Science and 
 Technology (CONACyT) that funded my curiosity-driven research which 
 allow me to quench this endless thirst for new knowledge during my 

appendixA/appendix.tex

@@ -85,37 +85,12 @@ \section{20 sample length vector.}
 \end{equation}
 %%********************************[EQUATION]************************************
 
-%With that in mind, the values of $\boldsymbol{X}^5_3$ in each row are the embedded
-%values from $ \{  \boldsymbol{x}_n  \} $. For instance, $\boldsymbol{X}[13]$ is built
-%from the following values of $ \{  \boldsymbol{x}_n  \} $:
-%$ \{    x_1, x_4,x_{7},x_{10},x_{13}    \}  $.
-%
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% E)eeeeee X)    xx T)tttttt R)rrrrr    A)aa
-% E)        X)  xx     T)    R)    rr  A)  aa
-% E)eeeee    X)xx      T)    R)  rrr  A)    aa
-% E)         X)xx      T)    R) rr    A)aaaaaa
-% E)        X)  xx     T)    R)   rr  A)    aa
-% E)eeeeee X)    xx    T)    R)    rr A)    aa
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% It is important to note that when thinking about embedding, the down-sampled
-% values of $\boldsymbol{v}_n$ are embedded into $\boldsymbol{V}[]$.
-% the rows of the matrix can be viewed as down-sampled vectors by $\tau$ (three in this case).
-
-
-
 
 \section{Time series for horizontal movement of a triaxial accelerometer.}
 In this example, it is considered a time series of a triaxial accelerometer 
 (Figure~\ref{fig:acc}(C)),
 captured from repetitions of a horizontal trajectory (Figure~\ref{fig:acc}(A))
 performed by user (Figure~\ref{fig:acc}(B)).
-%The trajectory is indicated by the points \textbf{a} and \textbf{b} which also 
-%indicate  a click sound in order to constraint the movement of the user 
-%so as to be consistent and synchronised (Figure~\ref{fig:acc}(A)).
-%The click sound is 60 beats per minute which means that there is a click sound
-%every second, so for this experiment the beat is only reproduced for 20 seconds.
 From Figure~\ref{fig:acc}(C)) is evidently that the $A_y(n)$ is 
 the most affected axis of the accelerometer due to the movement's 
 characteristics in the horizontal trajectory.
@@ -213,43 +188,3 @@ \section{Time series for horizontal movement of a triaxial accelerometer.}
 
 
 
-
-%
-%%%---------------------------------(FIGURE)-------------------------------------
-%\begin{figure}
-% \centering
-%   \includegraphics[width=1.0\textwidth]{figures/appendices/appendixA/sketchs_for_trajectories/figures/trajectory1a/aAhorizontal00}
-%   \caption{
-%	(A). Triaxial accelerometer (in red) is moved repetitively across a line
-%   	of 251 mm from point \textbf{a} to \textbf{b} and then from
-%   	\textbf{b} to \textbf{a}. 
-%   	The points \textbf{a} and \textbf{b} indicate when a click sound is produced.
-%	(B). Person's hand moving the sensor horizontally across the line.
-%	}
-%   \label{fig:acc}
-%\end{figure}
-%%%---------------------------------(FIGURE)-------------------------------------
-%
-%
-%
-%%%---------------------------------(FIGURE)-------------------------------------
-%\begin{figure}
-% \centering
-%   \includegraphics[width=1.0\textwidth]{figures/appendices/appendixA/outcomes/figure_second_experiment/fig_2E_v03}
-%   \caption{Time series for the triaxial accelerometer ($A_x(n)$, $A_y(n)$, $A_z(n)$) for
-%   ten repetitive horizontal movements across a line. The top time series only
-%   shows $A_y$ axis which corresponds to one cycle of the horizontal movement.
-%   The black arrows with sensors represent the movement's direction of the
-%   accelerometer with respect to the produced time series.
-%   }
-%   \label{fig:tsacc}
-%\end{figure}
-%%%---------------------------------(FIGURE)-------------------------------------
-%
-%
-%
-
-
-
-
-

appendixB/appendix.tex

@@ -10,14 +10,8 @@
 %    \graphicspath{{appendixB/figs/vector/}{appendixB/figs/}}
 %\fi
 
-
 \graphicspath{{figs/appendixB/PDF/}}
 
-%
-%\appendix
-%\hypertarget{aA}{ \section*{Appendix B. Inertial Sensors}}  \label{appendix:b}
-%
-
 
 \section{NeMEMsi IMU sensors}  \label{appendix:imus}
 For this work, data were collected using NeMEMsi sensors \cite{Comotti2014}
@@ -108,13 +102,6 @@ \subsection{Organising Data in Multidimensional Arrays}
 time using using \texttt{finddelay()} and \texttt{alignsignals()} 
 in \MATLAB.
 
-%The use of \texttt{alignsignals()} is useful when the data is relatively clean
-%that means that when data was noisy the alignment were not even close
-%when two signals were quite similar. Therefore, I decided to
-%program my own \texttt{alignsignalsMX()} to use the synchronised
-%data but with different length.
-%Scripts in \R  were used for postprocessing the data.
-
 \subsection{Data Synchornisation}
 To find the delay between two two sensors that were attached to the same place
 of the body parts, a function called  \texttt{finddelayMX()} was created.

appendixC/README.md

@@ -2,9 +2,14 @@
 
 #todo
 
-* [ ] fill info for AC
+
+
+# sorted 
+
+* [x] fill info for Appendix C
 Wed 12 Sep 12:21:21 BST 2018
 
+sorted: Sun 28 Oct 20:58:41 GMT 2018
 
 
 

appendixD/README.md

@@ -1,10 +1,23 @@
 
 
 #todo
+* [ ] Organise and balance the description of the results.
+
+Sun 28 Oct 21:02:55 GMT 2018
 
-* [ ] add figures for timeseries, RSSs, RPs, and RQA
+* [ ] Refer each of the extra information in this appendix 
+	with the description given in the chapters
+
+Sun 28 Oct 21:02:58 GMT 2018
+
+
+
+# sorted
+
+* [x] add figures for timeseries, RSSs, RPs, and RQA
 Fri 14 Sep 12:01:30 BST 2018
 
+sorted: 26 October 2018
 
 
 

appendixE/README.md

@@ -2,8 +2,21 @@
 
 #todo
 
-* [ ] add figures for timeseries, RSSs, RPs, and RQA
+
+* [ ] Organise and balance the description of the results.
+
+Sun 28 Oct 21:02:55 GMT 2018
+
+* [ ] Refer each of the extra information in this appendix 
+	with the description given in the chapters
+
+Sun 28 Oct 21:02:58 GMT 2018
+
+
+# sorted
+* [x] add figures for timeseries, RSSs, RPs, and RQA
 Fri 14 Sep 12:01:30 BST 2018
+sorted: 26 October 2018
 
 
 

appendixE/appendix.tex

@@ -491,18 +491,7 @@ \section{RPs} \label{appendix:e:rps}
 \newpage
 \section{RQAs} \label{appendix:e:rpas}
 \subsection{REC values}
-%It can be seen in Figs~\ref{fig:rec_aH} and \ref{fig:rec_aV} 
-%that REC values, representing the \% of black dots in the RPs, 
-%are more spread for HN than HF movements with time 
-%series coming from HS01 sensor. 
-%In contrast, REC values appear to be constant and present little variation 
-%for both HN and HF movements with time series from the sensor attached 
-%to the humanoid robot RS01.
-%With regard to the increase of smoothness of time series 
-%(sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ), REC values present little 
-%variation as the smoothness is increasing for time series from HS01 and 
-%REC values more similar as the smoothness is increasing for data from RS01.
-%
+
 
 REC values, representing the \% of black dots in the RPs, 
 are shown in Figs. \ref{fig:rec_aH} and \ref{fig:rec_aV}.

chapter1/README.md

@@ -1,15 +1,14 @@
 
 
 
-* [ ] Update IMAGE for Structure of the thesis
+* [x] Update IMAGE for Structure of the thesis
 
 added: Fri  7 Sep 13:49:41 BST 2018
+sorted: 24 October 2018
 
 
 
-
-
-* [ ] restate the research questions with the style of \cite{pincus1991}:
+* [ ] research questions can be restated using the following similar \citep{pincus1991}:
 
 ```
 The purpose of this paper is to give a preliminary mathematical