thesis draft v2.75

README.md

@@ -9,3 +9,25 @@ It is recommended to read the instructions on how to use the template
 
 There is a README.md in each Chapter to refer to the original chapter1.tex file
 and also as a logbook for any changes.
+
+
+
+## Citation
+
+If you use or adapt any of the files in this repository,
+use the following BibTeX to cite [my PhD thesis](http://).
+
+```
+@phdthesis{XochicalePhDThesis2018,
+	author = {Xochicale Miguel},
+    	month = {10},
+    	Year = {2018},
+    	school = {University of Birmingham},
+  	address = {Birmingham, United Kingdom},
+    	Title = {Nonlinear Analyses to Quantify Movement Variability in Human-Humanoid Interaction},
+  	type = {{PhD} dissertation as submitted},
+}
+```
+
+
+

appendixD/appendixD.tex → appendixD/appendix.tex

@@ -1,7 +1,7 @@
 %*******************************************************************************
-%****************************** Appendix C *************************************
+%****************************** Appendix D *************************************
 %*******************************************************************************
-		\chapter{Results for all data} \label{appendix:d}
+\chapter{Additional Results for HII experiment} \label{appendix:d}
 
 
 

appendixE/README.md

@@ -0,0 +1,10 @@
+
+
+#todo
+
+* [ ] add figures for timeseries, RSSs, RPs, and RQA
+Fri 14 Sep 12:01:30 BST 2018
+
+
+
+

appendixE/appendix.tex

@@ -0,0 +1,313 @@
+%*******************************************************************************
+%****************************** Appendix E *************************************
+%*******************************************************************************
+\chapter{Additional results for HHI experiment} \label{appendix:e}
+
+
+%% **************************** Define Graphics Path **************************
+%\graphicspath{{chapter7/figs/raster/}{chapter7/figs/PDF/}{chapter7/figs/}}
+\graphicspath{{figs/chapter6/PDF/}}
+
+
+\LaTeX .cls  files can be accessed system-wide when they are placed in the
+<texmf>/ tex/ latex directory, where <texmf> is the root directory of the user’s
+\TeX installation.
+On systems that have a local texmf tree (<texmflocal>), which
+may be named ``texmf-local'' or ``localtexmf'', it may be advisable to install
+packages in <texmflocal>, rather than <texmf> as the contents of the former,
+unlike that of the latter, are preserved after the \LaTeX system is reinstalled
+and/or upgraded.
+
+
+\section{Time Series} \label{appendix:e:ts}
+
+
+\section{Embedding parameters} \label{appendix:e:ep}
+
+
+
+%%---------------------------------(FIGURE)-------------------------------------
+%\begin{figure}[!h]
+\begin{figure}
+\centering
+\includegraphics[width=1.0\textwidth]{cao_aHw10}
+	\caption{
+	{\bf Minimum embedding dimensions for horizontal arm movements.} 
+		(A, B) Horizontal Normal (HN), (C, D) Horizontal Faster (HF) 
+		movements,
+		(A, C) sensor attached to participants (HS01), and
+		(B, D) sensor attached to robot (RS01).
+		Minimum embedding dimensions are for twenty participants 
+		(p01 to p20) with three smoothed signals 
+		(sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ)
+		and window lenght of 10-sec (500 samples).
+		R code to reproduce the figure is available 
+		from \cite{hwum2018}.
+        }
+    \label{fig:caoH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+%%---------------------------------(FIGURE)-------------------------------------
+%\begin{figure}[!h]
+\begin{figure}
+\centering
+\includegraphics[width=1.0\textwidth]{cao_aVw10}
+	\caption{
+	{\bf Minimum embedding dimensions for vertical arm movements.} 
+		(A, B) Vertical Normal (VN), (C, D) Vertical Faster (VF) 
+		movements,
+		(A, C) sensor attached to participants (HS01), and		
+		(B, D) sensor attached to robot (RS01).
+		Minimum embedding dimensions are for twenty participants 
+		(p01 to p20) with three smoothed signals (sg0zmuvGyroY, 
+		sg1zmuvGyroY and sg2zmuvGyroY) 
+		and window length of 10-sec (500 samples).
+		R code to reproduce the figure is available 
+		from \cite{hwum2018}.
+        }
+    \label{fig:caoV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+
+%%---------------------------------(FIGURE)-------------------------------------
+%\begin{figure}[!h]
+\begin{figure}
+\centering
+\includegraphics[width=1.0\textwidth]{ami_aHw10}
+	\caption{
+	{\bf First minimum AMI values for horizontal arm movements.}
+		(A, B) Horizontal Normal (HN), (C, D) Horizontal Faster (HF) 
+		movements,
+		(A, C) sensor attached to participants (HS01), and
+		(B, D) sensor attached to robot (RS01).
+		First minimum AMI values are for twenty participants 
+		(p01 to p20) with three smoothed signals (sg0zmuvGyroZ, 
+		sg1zmuvGyroZ and sg2zmuvGyroZ) and  window lenght of 
+		10-sec (500 samples).
+		R code to reproduce the figure is available 
+		from \cite{hwum2018}.
+        }
+    \label{fig:amiH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+%%---------------------------------(FIGURE)-------------------------------------
+%\begin{figure}[!h]
+\begin{figure}
+\centering
+\includegraphics[width=1.0\textwidth]{ami_aVw10}
+	\caption{
+	{\bf First minimum AMI values for vertical arm movements.}
+		(A, B) Vertical Normal (VN), (C, D) Vertical Faster (VF) 
+		movements,
+		(A, C) sensor attached to participants (HS01), and
+		(B, D) sensor attached to robot (RS01).
+		First minimum AMI values are for twenty participants 
+		(p01 to p20) with three smoothed signals (sg0zmuvGyroZ, 
+		sg1zmuvGyroZ and sg2zmuvGyroZ) and  window lenght of 
+		10-sec (500 samples).
+		R code to reproduce the figure is available 
+		from \cite{hwum2018}.
+        }
+    \label{fig:amiV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+
+
+\section{RSSs} \label{appendix:e:rsss}
+\section{RPs} \label{appendix:e:rps}
+
+
+\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.
+
+
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{rec_aH}
+    \caption{
+	{\bf REC values for horizontal arm movements.}	
+	REC values (representing \% of black dots in the RPs) for 
+	20 participants performing HN and HF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroZ (sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ).
+	REC values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:rec_aH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{rec_aV}
+    \caption{
+	{\bf REC values for vertical arm movements.}	
+	REC values (representing \% of black dots in the RPs) for 
+	20 participants performing VN and VF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroY (sg0zmuvGyroY, sg1zmuvGyroY and sg2zmuvGyroY).
+	REC values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:rec_aV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+\subsection{DET values}
+DET values, representing predictability and organisation of the RPs, 
+change very little even for type of movement and type of sensor
+(Figs~\ref{fig:det_aH} and \ref{fig:det_aV}).
+With regard to the smoothness of time series, DET values appear 
+to be more similar as the smoothness of the time series is increasing.
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{det_aH}
+    \caption{
+	{\bf DET values for horizontal arm movements.}	
+    	DET values (representing predictability and organisation of the RPs)
+	for 20 participants performing HN and HF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroZ (sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ).
+	DET values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:det_aH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{det_aV}
+    \caption{
+	{\bf DET values for vertical arm movements.}	
+    	DET values (representing predictability and organisation of the RPs) 
+	for 20 participants performing VN and VF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroY (sg0zmuvGyroY, sg1zmuvGyroY and sg2zmuvGyroY).
+	DET values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:det_aV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+
+\subsection{RATIO values}
+RATIO values, representing dynamic transitions, for both horizontal and
+vertical movements (Figs~\ref{fig:ratio_aH} and \ref{fig:ratio_aV})
+vary less for HN movements than HF movements for HS01 sensor 
+which is a similar behaviour of RATIO values for RS01 sensor.
+It can also noticed a decrease of variation in RATIO values as the 
+smoothness of the time series is increasing.
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{ratio_aH}
+    \caption{
+	{\bf RATIO values for horizontal arm movements.}
+	RATIO (representing dynamic transitions) for 
+	20 participants performing HN and HF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroZ (sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ).
+	RATIO values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:ratio_aH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{ratio_aV}
+    \caption{
+	{\bf RATIO values for vertical arm movements.}
+	RATIO (representing dynamic transitions) for 
+	20 participants performing VN and VF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroY (sg0zmuvGyroY, sg1zmuvGyroY and sg2zmuvGyroY).
+	RATIO values were computed with
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:ratio_aV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+
+\subsection{ENTR values}
+ENTR values, representing the complexity of the deterministic structure 
+of the time series, for both horizontal and vertical movements 
+(Figs~\ref{fig:entr_aH} and \ref{fig:entr_aV}) show more variation 
+for HS01 sensor than ENTR values for RS01 sensor which appear 
+to be more constant. 
+Generally, it can also be said that the smoothness of time series affects 
+little to the variation of ENTR values.
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{entr_aH}
+    \caption{
+	{\bf ENTR values for horizontal arm movements.}
+    	ENTR values (representing the complexity of the deterministic 
+	structure in time series) for 
+	20 participants performing HN and HF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroZ (sg0zmuvGyroZ, sg1zmuvGyroZ and sg2zmuvGyroZ).
+	ENTR values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:entr_aH}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+%%---------------------------------(FIGURE)-------------------------------------
+\begin{figure}[!h]
+\centering
+\includegraphics[width=1.0\textwidth]{entr_aV}
+    \caption{
+	{\bf ENTR values for vertical arm movements.}
+    	ENTR values (representing the complexity of the deterministic 
+	structure in time series) for 
+	20 participants performing VN and VF movements
+	with sensors HS01, RS01 and three smoothed-normalised axis 
+	of GyroY (sg0zmuvGyroY, sg1zmuvGyroY and sg2zmuvGyroY).
+	ENTR values were computed with 
+	embedding parameters $m=6$, $\tau=8$ and $\epsilon=1$.
+	R code to reproduce the figure is available from \cite{hwum2018}.
+        }
+    \label{fig:entr_aV}
+\end{figure}
+%%---------------------------------(FIGURE)------------------------------------
+
+
+
+
+

chapter1/README.md

@@ -1,7 +1,7 @@
 
 
 
-* [ ] Update Structure of the thesis
+* [ ] Update IMAGE for Structure of the thesis
 
 added: Fri  7 Sep 13:49:41 BST 2018