diff --git a/07-26_v1.tar.gz b/07-26_v1.tar.gz
deleted file mode 100644
index 872c015..0000000
Binary files a/07-26_v1.tar.gz and /dev/null differ
diff --git a/chap2/literature_review.tex b/chap2/literature_review.tex
index ef66a77..e5480d5 100644
--- a/chap2/literature_review.tex
+++ b/chap2/literature_review.tex
@@ -301,12 +301,12 @@ to measure speed are
   time in seconds taken to transcribe $T$, $\frac{1}{5}$ the average word length
   and $60$ the conversion to minutes. $|T| - 1$ counteracts the first input
   which starts the timer in many typing tests \cite{mackenzie_metrics}.
-  \item \textbf{\Gls{AdjWPM}} is especially useful if participants are allowed to
-  make mistakes and at the same time not forced to correct them. This method adds
-  an adjustable factor to lower the \gls{WPM} proportionally to the uncorrected
-  error rate $UER := [0;1]$ defined in Eq. (\ref{eq:uer}). The exponent $a$ in
-  Eq. (\ref{eq:cwpm}) can be chosen depending on the desired degree of correction
-  \cite{mackenzie_metrics}.
+  \item \textbf{\Gls{AdjWPM}} is especially useful if participants are allowed
+  to make mistakes and at the same time not obligated to correct them. This
+  method adds an adjustable factor to lower the \gls{WPM} proportionally to the
+  uncorrected error rate $UER := [0;1]$ defined in Eq. (\ref{eq:uer}). The
+  exponent $a$ in Eq. (\ref{eq:cwpm}) can be chosen depending on the desired
+  degree of correction \cite{mackenzie_metrics}.
   \begin{equation}\label{eq:cwpm}
     AdjWPM = WPM * (1 - UER)^{a}
   \end{equation}
diff --git a/chap3/implementation.tex b/chap3/implementation.tex
index 369e826..f5db65b 100644
--- a/chap3/implementation.tex
+++ b/chap3/implementation.tex
@@ -58,20 +58,21 @@ The platform offers three major functionalities that are important for this thes
   \item \textbf{The typing test} itself was designed after evaluating various
   free typing test tools online. One major issue almost all had in common was
   the lack of functionality to provide own texts for transcription. Further,
-  only a few provided insights on how performance metrics were calculated or
-  provided the ability to export results automatically. Since time in between
-  typing tests was limited by the design of the experiment as described in
-  Section \ref{sec:methodology}, recording the results by hand for multiple metrics
+  only a few provided insights on how performance metrics were calculated or the
+  ability to export results automatically. Since time in between typing tests
+  was limited by the design of the experiment as described in Section
+  \ref{sec:methodology}, recording the results by hand for multiple metrics
   would have been error prone and therefore not a valid option.
 
   The typing test provided by \gls{GoTT} features a non-intrusive interface. The
-  font size can be adjusted via the zoom functionality of the browser and colors
+  font size can be adjusted via the zoom functionality of the browser. Colors
   used to indicate correctly or incorrectly entered characters have been
   adjusted to enhance accessibility for people with vision related
   disabilities. The perception of the colors used in \gls{GoTT} for people with
   different color vision impairments can be observed in Figure
   \ref{fig:gott_colorblind} and was simulated with the help of a tool called
-  \textit{Color Oracle} \footnote{\url{https://colororacle.org/index.html}} \cite{colororacle}.
+  \textit{Color Oracle} \footnote{\url{https://colororacle.org/index.html}}
+  \cite{colororacle}.
 
   \begin{figure}[H]
     \centering
@@ -84,10 +85,10 @@ The platform offers three major functionalities that are important for this thes
   \end{figure}
 
   The typing test features an area to display the text that has to be
-  transcribed. As soon as the typist transcribed half of the displayed text, the
-  content of this area starts scrolling up one line after each finished line of
-  text. Further, two drop down menus are used to select the text and keyboard
-  currently required for the next typing test. Lastly, two buttons control when
+  transcribed. As soon as the typist has transcribed half of the displayed text,
+  the content of this area starts to scroll up one line after each finished line
+  of text. Further, two drop down menus are used to select the text and keyboard
+  currently required for the next typing test. Lastly, two buttons determine when
   the text is revealed (Start) and if the participant or researcher wants to
   interrupt the active typing test in case of malfunctioning hardware e.g.,
   keyboard, \gls{EMG} device, computer, etc., or if the subject experiences
@@ -144,14 +145,14 @@ KSPS = roundToPrecision((ISL - 1) / TEST_TIME, 5);
 % KSPC = roundToPrecision(ISL / TL, 5);
 
 For further implementation details on how input was captured or sent to the
-backend, refer to the code in the online
+backend refer to the code in the online
 repository\footnote{\url{https://github.com/qhga/GoTT}}.
 
-To test the usability of the typing test, we asked five individuals to complete
+To test the usability of the typing test we asked five individuals to complete
 multiple typing tests with their own computer. Based on the feedback we
 received, we were able to switch to another font to further improve readability
-and also fix a bug related to the scrolling. All five testers reported that the
-typing test was very intuitive and fun to use.
+and also fix a bug related to the scrolling. All five volunteers reported that
+the typing test was very intuitive and fun to use.
 
 \item \textbf{The questionnaires} had to be linked to a specific participant,
 typing test and keyboard. In total, three different types of questionnaires had
@@ -160,14 +161,13 @@ Section \ref{sec:methodology}). The demographics questionnaire was completed
 once at the start of the experiment, which could have been done via already
 existing survey tools and then linked to the participant by hand. The \gls{PTTQ}
 and the \gls{PKQ} on the other hand, were required after each individual typing
-test or after every keyboard respectively. To manually match all finished
-questionnaires to the corresponding typing tests and keyboards, could introduce
-an unwanted source of errors. Therefore, we implemented a survey tool into
-\gls{GoTT} which automatically matched completed questionnaires to typing tests
-and keyboards. The \gls{PTTQ} resembled the \gls{KCQ} \cite[56]{iso9241-411} and
-the questions for the \gls{PKQ} were gathered from the \gls{UEQ-S}
-\cite{schrepp_ueq_handbook}. All questionnaires can be observed in Appendix
-\ref{app:gott}.
+test or after every keyboard respectively. Whereas manually matching all
+finished questionnaires to the corresponding typing tests and keyboards could
+have led to unwanted errors, we decided to implement a survey tool into
+\gls{GoTT} which achieved this task automatically. The \gls{PTTQ} resembled the
+\gls{KCQ} \cite[56]{iso9241-411} and the questions for the \gls{PKQ} were
+gathered from the \gls{UEQ-S} \cite{schrepp_ueq_handbook}. All questionnaires
+can be observed in Appendix \ref{app:gott}.
 
 \item \textbf{The text crowdsourcing platform} was required because of the
 potential introduction of observer bias as described in Section
@@ -187,8 +187,8 @@ with $n_{kb}$ the number of tested keyboards, $m_{ttkb}$ the number of typing
 test conducted with each keyboard, $\frac{s}{60}$ the time for each typing test
 (5min), $|w|$ number of characters defining a word (Section \ref{sec:meas_perf})
 and $wpm_{max}$ which represents the average wpm of the top 100 typists
-retrieved from a database released by the website Typeracer
-\footnote{\url{https://docs.google.com/spreadsheets/d/18ZokmvjdzDypIr-Ayl1VWsRPOBa91qvgX3FgcsZtSAU/edit#gid=636312661}}
+retrieved from a database released by the website
+Typeracer\footnote{\url{https://docs.google.com/spreadsheets/d/18ZokmvjdzDypIr-Ayl1VWsRPOBa91qvgX3FgcsZtSAU/edit#gid=636312661}}
 which included the top 25000 competitors in terms of average \gls{WPM}
 \cite{typeracer}.
 
@@ -204,7 +204,7 @@ requirements:
 In order to communicate what kind of text is appropriate, the platform provided
 an example where the difference between fairly easy and difficult text was
 shown. Further, the backend implemented a set of functions that calculated the
-\gls{FRE} of submitted text and also counted the number of characters and either
+\gls{FRE} of submitted text, counted the number of characters and either
 accepted or rejected the text depending on if the requirements were met or
 not. The implementation of the algorithm that calculates the \gls{FRE} can be
 seen in Listing \ref{lst:gott_fre}. The function \textit{countSyllables}
@@ -218,13 +218,13 @@ with the help of multiple unit tests and also compared to scores obtained by
 another website \footnote{\url{https://fleschindex.de/berechnen/}} offering the
 calculation for German texts. The \gls{UI} for the crowdsourcing page is shown
 in Appendix \ref{app:gott}. The gathered text snippets were, first checked for
-typos using \textit{Duden Mentor}\footnote{\url{https://mentor.duden.de/}},
+typos and grammar using \textit{Duden Mentor}\footnote{\url{https://mentor.duden.de/}},
 then randomized and finally aggregated into equally long texts with nearly
 identical \gls{FRE} scores (mean = 80.10, SD = 0.48).
 
 \begin{listing}[H]
 \caption{Algorithm that calculates the \gls{FRE} score for a given string in German
-language, utilizing regex pattern matching to count syllable, words and sentences.}
+language, utilizing regex pattern matching to count syllables, words and sentences.}
 \label{lst:gott_fre}
 \begin{minted}[linenos,fontsize=\small]{go}
 func countSyllables(txt string) int {
@@ -284,31 +284,32 @@ func calculateFRE(txt string) float64 {
   \label{fig:force_master}
 \end{figure}
 
-Because we required very specific data about the force each digit is able to
-apply to keyswitches in different locations, we decided to prototype our own
-device to measure the required data. Because of previous research in the field
-of finger strength and force applied to keyboards, we wanted to use the same
-type of sensor―a load cell―that was commonly utilized in those studies
-\cite{gerard_keyswitch, rempel_ergo, bufton_typingforces}. A load cell, capable
-of measuring up to 5 kg $\approx$ 49.0 \gls{N}, in combination with the HX711
-load cell amplifier shown in Figure \ref{fig:hx711} and the library
-HX711\_ADC\footnote{\url{https://github.com/olkal/HX711_ADC}} was used to build
-the prototype which can be seen in Figure \ref{fig:force_master}. Initial
-testing revealed, that the response for measurements with the standard 10 Hz
-sample rate of the HX711 was not sufficient to pick up the peak force in some
-measurements. Therefore we resoldered the 0 $\Omega$ surface mount resistor to
-raise sample rate to 80 Hz, which yielded better results for fast keystrokes but
-did not deteriorate overall precision compared to the measurements conducted
-with 10 Hz. The apparatus used an \gls{OLED} display to present currently
-applied force in gram and peak force in gram and \gls{N}. The devices was mainly
-controlled via two terminal commands. One command initiated re-calibration that
-was used after each participant or in between measurements and the other command
-reset all peak values displayed via the display. The base of the device featured
-a scale, which was traversed with the help of a wrist rest that got aligned
-with the markings corresponding to the currently measured key. Each mark
-represents the distance and position of a finger to the associated key indicated
-by the label underneath the marking. The measurement process is explained in
-more detail in Section \ref{sec:meth_force}
+Considering the fact that we required very specific data about the force each
+digit is able to apply to keyswitches in different locations, we decided to
+prototype our own device to measure the required data. Because of previous
+research in the field of finger strength and force applied to keyboards, we
+wanted to use the same type of sensor―a load cell―that was commonly utilized in
+those studies \cite{gerard_keyswitch, rempel_ergo, bufton_typingforces}. A load
+cell, capable of measuring up to 5 kg $\approx$ 49.0 \gls{N}, in combination
+with the HX711 load cell amplifier shown in Figure \ref{fig:hx711} and the
+library HX711\_ADC\footnote{\url{https://github.com/olkal/HX711_ADC}} was used
+to build the prototype which can be seen in Figure
+\ref{fig:force_master}. Initial testing revealed that the response for
+measurements with the standard 10 Hz sample rate of the HX711 was not sufficient
+to pick up the peak force in some measurements. Therefore, we resoldered the 0
+$\Omega$ surface mount resistor to raise sample rate to 80 Hz, which yielded
+better results for fast keystrokes but did not deteriorate overall precision
+compared to the measurements conducted with 10 Hz. The apparatus used an
+\gls{OLED} display to present currently applied force in gram and peak force in
+gram and \gls{N}. The device was mainly controlled via two terminal
+commands. While one command initiated re-calibration that was used after each
+participant or in between measurements, the other command reset all peak
+values displayed via the display. The base of the device featured a scale, which
+was traversed with the help of a wrist rest that got aligned with the markings
+corresponding to the currently measured key. Each mark represents the distance
+and position of a finger to the associated key indicated by the label underneath
+the marking. The measurement process is explained in more detail in Section
+\ref{sec:meth_force}
 
 \begin{figure}[ht]
   \centering
@@ -323,13 +324,13 @@ more detail in Section \ref{sec:meth_force}
 \subsection{Summary}
 By implementing our own typing test platform (\gls{GoTT}) we maximized the
 control over one of the main measurement tools required by our experiment. We
-were able to exactly define all functions responsible to collect the metrics,
+were able to exactly define all functions responsible to collect the metrics
 according to our research done in Section \ref{sec:meas_perf}. The crowdsourcing
 tool allowed us to gather a great amount of unbiased text in very little time
 and the addition of questionnaires into \gls{GoTT} eliminated the possibility of
 unnecessary errors. Both potentially improved the reliability of the results
 acquired by our experiment. Further, the device we built to measure the peak
-force each finger can produce while pressing certain keys on a keyboard, allowed
+force each finger can produce while pressing certain keys on a keyboard allowed
 us to base the design of our keyboard with non-uniform actuation forces on more
-then anecdotal evidence. The exact procedure of our preliminary experiment on
+than anecdotal evidence. The exact procedure of our preliminary experiment on
 peak force will be addressed in the following section.
diff --git a/chap4/methodology.tex b/chap4/methodology.tex
index 0952806..8941f73 100644
--- a/chap4/methodology.tex
+++ b/chap4/methodology.tex
@@ -2,7 +2,7 @@
 \label{sec:methodology}
 \subsection{Research Approach}
 Because of the controversial findings about the impact of key actuation forces
-on speed \cite{akagi_keyswitch, loricchio_force_speed} and the fact, that
+on speed \cite{akagi_keyswitch, loricchio_force_speed} and the fact that
 keyboard related work can increase the risk for \gls{WRUED} \cite{ccfohas_wrued,
   pascarelli_wrued}, we decided to further investigate possible effects of
 different actuation forces and even a keyboard equipped with non-uniform
@@ -12,16 +12,16 @@ non-uniform actuation forces on these metrics. Therefore, we first asked
 seventeen people about their preferences, experiences and habits related to
 keyboards to get a better understanding on what people might prefer as a
 baseline for the design of the adjusted keyboard (keyboard with non-uniform
-actuation forces) and to complement the findings obtained through our literature
-review. Further, we collected information about available mechanical keyswitches
-on the market. Additionally, we conducted a small preliminary experiment with 6
-subjects, where we measured the peak forces each individual finger of the right
-hand was able to apply to distinct keys in different locations. We then created
-the design for the adjusted keyboard based on those measurements. Lastly, an
-experiment with twenty-four participants was conducted, where we compared the
-performance and user satisfaction while using four different keyboards,
-including our adjusted keyboard. Figure \ref{fig:s4_flow} presents a brief
-overview of the consecutive sections.
+actuation forces) as well as to complement the findings obtained through our
+literature review. Further, we collected information about available mechanical
+keyswitches on the market. Additionally, we conducted a small preliminary
+experiment with 6 subjects, where we measured the peak forces each individual
+finger of the right hand was able to apply to distinct keys in different
+locations. We then created the design for the adjusted keyboard based on those
+measurements. Lastly, an experiment with twenty-four participants was conducted,
+where we compared the performance and user satisfaction while using four
+different keyboards, including our adjusted keyboard. Figure \ref{fig:s4_flow}
+presents a brief overview of the consecutive sections.
 
 \begin{figure}[H]
   \centering
@@ -60,7 +60,7 @@ described by the seven who already experienced pain were the wrist
 review \cite{ergopedia_keyswitch, peery_3d_keyswitch}. Nine answered that they
 use a notebook (scissor-switches, membrane), six stated that they use an
 external keyboard with rubber dome switches and only two responded that they use
-a keyboard featuring mechanical keyswitches. The average, self-reported, usage
+a keyboard featuring mechanical keyswitches. The average―self-reported―usage
 ranged between half an hour and 10 hours with a mean of 4.71 hours. It is
 important to note, that a study by Mikkelsen et al. found, that self-reported
 durations related to computer work can be inaccurate
@@ -112,7 +112,7 @@ To evaluate the impact of an adjusted keyboard\footnote{keyboard with
   non-uniform actuation forces} on performance and satisfaction we first needed
 to get an understanding on how to distribute keyswitches with different
 actuation forces across a keyboard. Our first idea was to use a similar approach
-to the keyboard we described in Section \ref{sec:lr_sum}, were the force
+to the keyboard we described in Section \ref{sec:lr_sum}, where the force
 required to activate the keys decreased towards the left and right ends of the
 keyboard. This rather simple approach only accounts for the differences in
 finger strength when all fingers are in the same position, but omits possible
@@ -125,14 +125,14 @@ distributed as follows: computer science students (3/6), physiotherapist (1/6),
 user experience consultant (1/6) and retail (1/6). All Participants were given
 instructions to exert maximum force for approximately one second onto the key
 mounted to the measuring device described in Section
-\ref{sec:force_meas_dev}. We also used a timer to announced when to press and
+\ref{sec:force_meas_dev}. We also used a timer to announce when to press and
 when to stop. We provided a keyboard to every participant, which was used as a
 reference for the finger position before every measurement. To reduce order
 effects, we used a balanced latin square to specify the sequence of rows (top,
 home, bottom) in which the participants had to press the keys
 \cite{bradley_latin_square}. Additionally, because there were only six people
 available, we alternated the direction from which participants had to start in
-such a way, that every second subject started with the little finger instead of
+such a way that every second subject started with the little finger instead of
 the index finger. An example of four different positions of the finger while
 performing the measurements for the keys \textit{Shift, L, I} and \textit{Z} can
 be observed in Figure \ref{fig:FM_example}.
@@ -180,7 +180,7 @@ key can be seen in Eq. (\ref{eq:force_example}).
   AF_{P} = GFR * MAF_{P} = 3.23 \frac{g}{N} * 10.45\,N \approx 33.75\,g
 \end{equation}
 
-We then assigned the each theoretical actuation force to a group that resembles
+We then assigned each theoretical actuation force to a group that resembles
 a spring resistance which is available on the market or can be adjusted to that
 value. We matched the results from Table \ref{tbl:finger_force} to the groups
 representing the best fit shown in Table \ref{tbl:force_groups}.
@@ -231,7 +231,8 @@ representing the best fit shown in Table \ref{tbl:force_groups}.
     corresponding keyswitch in the following row. The columns indicate the label
     of the scale on the measuring device which can be seen in Figure
     \ref{fig:FM_example}. \textit{↑} stands for the shift key. \textit{F5} :=
-    little finger, ..., \textit{F2} := index finger}
+    little finger, \textit{F4} := ring finger, \textit{F3} := middle finger,
+    \textit{F2} := index finger}
   \label{tbl:finger_force}
 \end{table}
 
@@ -255,11 +256,12 @@ representing the best fit shown in Table \ref{tbl:force_groups}.
   \caption{Categorization of theoretical actuation forces acquired with
     Eq. (\ref{eq:actuation_forces}), into groups of more commonly available
     stiffnesses of springs. The rows indicate which finger is used to press the
-    key. \textit{F5} := little finger, ..., \textit{F2} := index finger}
+    key. \textit{F5} := little finger, \textit{F4} := ring finger, \textit{F3}
+    := middle finger, \textit{F2} := index finger}
   \label{tbl:force_groups}
 \end{table}
 
-We simply mirrored the results of the right hand, for keys operated by the left
+We simply mirrored the results of the right hand for keys operated by the left
 hand and copied the values to keys which are out of reach without lifting the
 hand. Finally, we created the adjusted keyboard layout that can be examined in
 Figure \ref{fig:adjusted_layout}. This layout was used in our main experiment
@@ -313,25 +315,26 @@ measured via \gls{EMG}, post experiment semi structured interview and ux-curves)
 \subsubsection{Participants}
 \label{sec:main_participants}
 There were no specific eligibility criteria for participants (n=24) of this
-study beside the ability to type on a keyboard for longer durations and with all
-ten fingers. The style used to type was explicitly not restricted to schoolbook
-touch typing to also evaluate possible effects of the adjusted keyboard on
-untrained typists. All participants recruited were personal contacts. 54\,\% of
-subjects were females. Participant's ages ranged from 20 to 58 years with a mean
-age of 29. Sixteen out of the twenty-four subjects (67\,\%) reported that they
-were touch typists. Subjects reported the following keyboard types as their
-daily driver, notebook keyboard (12, 50\,\%), external keyboard (11, 46\,\%) and
-split keyboard (1, 4\,\%). The keyswitch types of those keyboards were distributed
-as follows: scissor-switch (13, 54\,\%), rubber dome (8, 33\,\%) and mechanical
-keyswitches (3, 13\,\%). We measured the actuation force of each participants own
-keyboard and the resulting distribution of actuation forces can be observed in
-Figure \ref{fig:main_actuation_force}. The self-reported average daily usage of
-a keyboard ranged from 1 hour to 13 hours, with a mean of 6.69 hours. As already
-mentioned in Section \ref{sec:telephone_interview} it is important to note, that
-a study by Mikkelsen et al. found, that self-reported durations related to
-computer work can be inaccurate \cite{mikkelsen_duration}. All participants used
-the \gls{QWERTZ} layout and therefore were already used to the layout used
-throughout the experiment.
+study besides the ability to type on a keyboard for longer durations and with
+all ten fingers. The style used to type was explicitly not restricted to
+schoolbook touch typing to also evaluate possible effects of the adjusted
+keyboard on untrained typists. All participants recruited were personal
+contacts. 54\,\% of subjects were females. Participant's ages ranged from 20 to
+58 years with a mean age of 29. Sixteen out of the twenty-four subjects (67\,\%)
+reported that they were touch typists. Subjects reported the following keyboard
+types as their daily driver, notebook keyboard (12, 50\,\%), external keyboard
+(11, 46\,\%) and split keyboard (1, 4\,\%). The keyswitch types of those
+keyboards were distributed as follows: scissor-switch (13, 54\,\%), rubber dome
+(8, 33\,\%) and mechanical keyswitches (3, 13\,\%). We measured the actuation
+force of each participants own keyboard. The resulting distribution of actuation
+forces can be observed in Figure \ref{fig:main_actuation_force}. The
+self-reported average daily usage of a keyboard ranged from 1 hour to 13 hours,
+with a mean of 6.69 hours. As already mentioned in Section
+\ref{sec:telephone_interview} it is important to note, that a study by Mikkelsen
+et al. found, that self-reported durations related to computer work can be
+inaccurate \cite{mikkelsen_duration}. All participants used the \gls{QWERTZ}
+layout and therefore were already used to the layout used throughout the
+experiment.
 
 \begin{figure}[H]
   \centering
@@ -344,12 +347,12 @@ throughout the experiment.
 
 \subsubsection{Experimental Environment}
 \label{sec:main_environment}
-The whole experiments took place in a room normally used as an office. Chair,
-and table were both height adjustable. The armrests of the chair were also
+All the experiments took place in a room normally used as an office. Chair, and
+table were both height adjustable. The armrests of the chair were also
 adjustable in height and horizontal position. The computer used for all
-measurements featured an Intel i7-5820K (12) @ 3.600\,GHz processor, 16\,gB RAM and
-a NVIDIA GeForce GTX 980 Ti graphics card. The operating system on test machine
-was running \textit{Arch Linux}\footnote{\url{https://archlinux.org/}}
+measurements featured an Intel i7-5820K (12) @ 3.600\,GHz processor, 16\,gB RAM
+and a NVIDIA GeForce GTX 980 Ti graphics card. The operating system on test
+machine was running \textit{Arch Linux}\footnote{\url{https://archlinux.org/}}
 (GNU/Linux, Linux kernel version: 5.11.16). The setup utilized two 1080p (Full
 HD, Resolution: 1920x1080, Refresh-rate: 144Hz) monitors were participants were
 allowed to adjust the angle, height and brightness prior to the start of the
@@ -368,10 +371,10 @@ researchers were tested with antigen tests prior to every appointment.
 
 \subsubsection{Independent Variable: Keyboards}
 \label{sec:main_keyboards}
-Additionally to the reference tests conducted with the participant's own
-keyboards, we provided four keyboards which only differed in terms of actuation
-force (Appendix \ref{app:equipment}). We decided to assign pseudonyms in the
-form of Greek goddesses to the keyboards to make fast differentiation during the
+Alongside the reference tests conducted with the participant's own keyboards, we
+provided four keyboards which only differed in terms of actuation force
+(Appendix \ref{app:equipment}). We decided to assign pseudonyms in the form of
+Greek goddesses to the keyboards to make fast differentiation during the
 sessions easier and reduce ambiguity. The pseudonyms for each keyboard and the
 corresponding actuation force can be found in Table \ref{tbl:kb_pseudo}.
 
@@ -424,12 +427,12 @@ follows:
 \label{sec:main_design}
 \textbf{Preparation and Demographics}
 
-The whole laboratory experiment was conducted over a total time span of 3
-weeks. Participants were tested one at a time in sessions that in total took
+The whole laboratory experiment was conducted over a total time span of three
+weeks. Participants were tested one at a time in sessions that took in total
 $\approx$ 120 minutes. Prior to the evaluation of the different keyboards, the
 participant was instructed to read the terms of participation which included
 information about privacy, the \gls{EMG} measurements and questionnaires used
-during the experiment. Next, participants filled out a pre-experiment
+during the experiment. Next, the participants filled out a pre-experiment
 questionnaire to gather demographic and other relevant information e.g., touch
 typist, average \gls{KB} usage per day, predominantly used keyboard type,
 previous medical conditions affecting the result of the study e.g.,
@@ -459,19 +462,19 @@ was then confirmed, by observing the data received by the \textit{FlexVolt
 the participant performed flexion and extension of the wrist. The
 \textit{FlexVolt 8-Channel Bluetooth Sensor} used following hardware settings to
 record the data: 8-Bit sensor resolution, 32ms \gls{RMS} window size and
-Hardware smoothing filter turned off. To gather reference values (100\,\%\gls{MVC}
-and 0\,\%\gls{MVC}), which are used later to calculate the percentage of muscle
-activity for each test, we performed three measurements. First, participants
-were instructed to fully relax the \gls{FDS}, \gls{FDP} and \gls{ED} by
-completely resting their forearms on the table. Second, participants exerted
-maximum possible force with their fingers (volar) against the top of the table
-(\gls{MVC} - flexion) and lastly, participants applied maximum possible force
-with their fingers (dorsal) to the bottom of the table while resting their
-forearms on their thighs (\gls{MVC} - extension). We decided to also measure
-0\,\%\gls{MVC} before and after each typing test and used these values to
-normalize the final data instead of the 0\,\%\gls{MVC} we retrieved from the
-initial \gls{MVC} measurements. A picture of all participants with the attached
-electrodes can be observed in Appendix \ref{app:emg}.
+Hardware smoothing filter turned off. To gather reference values
+(100\,\%\gls{MVC} and 0\,\%\gls{MVC}), which are used later to calculate the
+percentage of muscle activity for each test, we performed three
+measurements. First, participants were instructed to fully relax the \gls{FDS},
+\gls{FDP} and \gls{ED} by completely resting their forearms on the
+table. Second, participants exerted maximum possible force with their fingers
+(volar) against the top of the table (\gls{MVC} - flexion). Lastly, participants
+applied maximum possible force with their fingers (dorsal) to the bottom of the
+table while resting their forearms on their thighs (\gls{MVC} - extension). We
+decided to also measure 0\,\%\gls{MVC} before and after each typing test and
+used these values to normalize the final data instead of the 0\,\%\gls{MVC} we
+retrieved from the initial \gls{MVC} measurements. A picture of all participants
+with the attached electrodes can be observed in Appendix \ref{app:emg}.
 
 \textbf{Familiarization with \glsfirst{GoTT} and the Keyboards}
 
@@ -484,8 +487,9 @@ Aphrodite (50\,g). Additionally, because of a possible height difference between
 choice to use wrist rests of adequate height in combination with all four
 keyboards during the experiment. If during this process participants reported
 that an electrode is uncomfortable and that it would influence the following
-typing test, this electrode was relocated and the procedure in the last section
-was repeated\footnote{Happened one time during the whole experiment}.
+typing test, this electrode was relocated and the procedure in the last
+paragraph\footnote{\gls{EMG} Measurements} was repeated\footnote{Happened one
+  time during the whole experiment}.
 
 \textbf{Texts Used for Typing Tests}
 
@@ -501,14 +505,14 @@ To receive feedback about several aspects that define a satisfactory user
 experience while using a keyboard, we decided to incorporate two questionnaires
 into our experiment. The first questionnaire was the \glsfirst{KCQ} provided by
 \cite[56]{iso9241-411} and was filled out after each individual typing test
-(\glsfirst{PTTQ}). The second survey, that was filled out every time the keyboard
-was changed, was the \glsfirst{UEQ-S} \cite{schrepp_ueq_handbook} with an
-additional question―``How satisfied have you been with this keyboard?''―that
-could be answered with the help of an \gls{VAS} ranging from 0 to 100
-(\glsfirst{PKQ})\cite{lewis_vas}. The short version of the \gls{UEQ} was selected, because of
-the limited time participants had to fill out the questionnaires in between
-typing tests (2 - 3 minutes) and also because participants had to rate multiple
-keyboards in one session \cite{schrepp_ueq_handbook}.
+(\glsfirst{PTTQ}). The second survey, that was filled out every time the
+keyboard was changed, was the \glsfirst{UEQ-S} \cite{schrepp_ueq_handbook} with
+an additional question―``How satisfied have you been with this keyboard?''―that
+could be answered with the help of a \gls{VAS} ranging from 0 to 100
+(\glsfirst{PKQ})\cite{lewis_vas}. Due to the limited time participants had to
+fill out the questionnaires in between typing tests (2 - 3 minutes) and also
+because participants had to rate multiple keyboards in one session, the short
+version of the \gls{UEQ} was selected \cite{schrepp_ueq_handbook}.
 
 \textbf{Post Experiment Interview \& \Gls{UX Curve}s}
 
@@ -518,12 +522,12 @@ tests were completed. We recorded audio and video for the whole duration of the
 interviews and afterwards categorized common statements about each
 keyboard.
 
-Further, we prepared two different graphs were participants had to draw
-\Gls{UX Curve}s related to subjectively perceived typing speed and subjectively
+Further, we prepared two different graphs were participants had to draw \Gls{UX
+  Curve}s related to subjectively perceived typing speed and subjectively
 perceived fatigue for every keyboard and corresponding typing test. The graphs
 always reflected the order of keyboards for the group the current participant
 was part of. Furthermore, before the interview started, participants were given
-a brief introduction on how to draw \Gls{UX Curve}s and that it is desirable to
+a brief introduction on how to draw \Gls{UX Curve}s and, that it is desirable to
 explain the thought process while drawing each curve \cite{kujala_ux_curve}. An
 example of the empty graph for perceived fatigue (group 1) can be seen in Figure
 \ref{fig:empty_ux_g1}.
@@ -538,24 +542,25 @@ example of the empty graph for perceived fatigue (group 1) can be seen in Figure
 
 \textbf{Main Part of the Experiment}
 
-Each subject had to take two, 5 minute, typing tests per keyboard, with a total
+Each subject had to take two, 5-minute-typing-tests per keyboard, with a total
 of 5 keyboards, namely \textit{Own (participant's own keyboard)}, \textit{Nyx
   (35\,g, uniform), Aphrodite (50\,g, uniform), Athena (80\,g uniform)} and
-\textit{Hera (35\,g - 60\,g, adjusted)} (Table \ref{tbl:kb_pseudo}). As described
-in Section \ref{sec:main_keyboards}, the order of the keyboards \textit{Nyx,
-  Aphrodite, Athena} and \textit{Hera} was counterbalanced with the help of a
-balanced latin square to reduce order effects. The keyboard \textit{Own} was
-used to gather reference values for all measured metrics. Thus, typing tests
-with \textit{Own} were conducted before (one test) and after (one test) all
-other keyboards, to also capture possible variations in performance due to
-fatigue. Participants were allowed, but not forced to, correct mistakes during
-the typing tests. The typing test application allowed no shortcuts to delete or
-insert multiple characters and correction was only possible by hitting the
-\textit{Backspace} key on the keyboard. The \textit{Capslock} key was disable
-during all typing tests, because there was only visual feedback in form of
-coloring of correct and incorrect input and no direct representation of entered
-characters (Figure \ref{fig:gott_colorblind}), which could lead to confusion
-when the \textit{Capslock} key is activated on accident.
+\textit{Hera (35\,g - 60\,g, adjusted)} (Table \ref{tbl:kb_pseudo}). As
+described in Section \ref{sec:main_keyboards}, the order of the keyboards
+\textit{Nyx, Aphrodite, Athena} and \textit{Hera} was counterbalanced with the
+help of a balanced latin square to reduce order effects. The keyboard
+\textit{Own} was used to gather reference values for all measured metrics. Thus,
+typing tests with \textit{Own} were conducted before (one test) and after (one
+test) all other keyboards, to also capture possible variations in performance
+due to fatigue. Participants were allowed, but not obligated to, correct
+mistakes during the typing tests. The typing test application allowed no
+shortcuts to delete or insert multiple characters and correction was only
+possible by hitting the \textit{Backspace} key on the keyboard. The
+\textit{Capslock} key was disabled during all typing tests, because there was
+only visual feedback in form of coloring of correct and incorrect input and no
+direct representation of entered characters (Figure \ref{fig:gott_colorblind}),
+which could have led to confusion when the \textit{Capslock} key was activated
+by accident.
 
 \subsection{Summary}
 \label{sec:meth_summary}
diff --git a/chap5/results.tex b/chap5/results.tex
index 8a536a0..dd82853 100644
--- a/chap5/results.tex
+++ b/chap5/results.tex
@@ -40,12 +40,12 @@ significant differences in \glsfirst{AdjWPM} for T0\_1 (M = 53.9, sd = 14.5) and
 T0\_2 (M = 52.5, sd = 14.3, t = 2.44, p = 0.023), \glsfirst{CER} for T0\_1 (M =
 0.057, sd = 0.028) and T0\_2 (M = 0.078, sd = 0.038, t = -3.54, p = 0.002) and
 \glsfirst{TER} for T0\_1 (M = 0.063, sd = 0.031) and T0\_2 (M = 0.086, sd =
-0.039, t = -4.27, p = 0.0003). Because of the differences, we decided to use the
+0.039, t = -4.27, p = 0.0003). Because of the differences we decided to use the
 means of all metrics gathered for each participant through T0\_1 and T0\_2 as
 the reference values to compute the \textit{\gls{OPC}} for the test keyboards
 (\textit{Athena, Aphrodite, Nyx} and \textit{Hera}). This value was later used
 to make statements about the performance of the individual test keyboards
-compared to the participant's own, familiar, keyboard.
+compared to the participant's own, familiar keyboard.
 
 Additionally, using a dependent T-test, we compared the muscle activity (\% of
 \glsfirst{MVC}) and found, that there are significant differences in left flexor
@@ -94,22 +94,22 @@ can be observed in Table \ref{tbl:res_own_before_after}.
 
 We also evaluated the means of \glsfirst{KCQ} questions 8 to 12 which concerned
 perceived fatigue in fingers, wrists, arms, shoulders and neck respectively
-(7-point Likert scale) and the slopes (improving, deteriorating, stable) of the
-\gls{UX Curve}s drawn by each participant after the whole experiment, to identify
-possible differences in perceived fatigue from T0\_1 to T0\_2. As shown in
-Figure \ref{fig:res_own_per_fat}, participants \gls{KCQ} reported slight
+(7-point Likert scale) as well as the slopes (improving, deteriorating, stable)
+of the \gls{UX Curve}s drawn by each participant after the whole experiment, to
+identify possible differences in perceived fatigue from T0\_1 to T0\_2. As shown
+in Figure \ref{fig:res_own_per_fat}, participants \gls{KCQ} reported slight
 improvements in terms of finger (diff = 0.33) and wrist (diff = 0.33) fatigue in
 T0\_2 compared to T0\_1, no difference in arm fatigue (diff = 0) and very
 slightly increased fatigue in shoulder (diff = -0.12) and neck (diff = -0.13) in
-T0\_2 compared to T0\_1. Sixteen of the twenty-four \gls{UX Curve}s regarding overall
-perceived fatigue had positive slope when measured from start of T0\_1 to end of
-T0\_2 ($\pm$ 1 mm). The subjective reports about the decrease in finger and
-wrist fatigue emphasize the decrease in muscle activity for the flexor muscles
-we described in the last paragraph.
+T0\_2 compared to T0\_1. Sixteen of the twenty-four \gls{UX Curve}s regarding
+overall perceived fatigue had positive slope when measured from start of T0\_1
+to end of T0\_2 ($\pm$ 1 mm). The subjective reports about the decrease in
+finger and wrist fatigue emphasize the decrease in muscle activity for the
+flexor muscles we described in the last paragraph.
 
 \begin{figure}[H]
   \centering
-  \includegraphics[width=1.0\textwidth]{images/res_own_per_fat}
+  \includegraphics[width=0.98\textwidth]{images/res_own_per_fat}
   \caption{Trends for reported fatigue through the \gls{KCQ} (questions 8:
     finger, 9: wrist, 10: arm, 11: shoulder, 12: neck) and histogram for the
     slopes (IM: improving, DE: deteriorating, ST: stable) of \gls{UX Curve}s
@@ -142,16 +142,16 @@ significant differences between \textit{Aphrodite} (M = 51.5, sd = 14.0) and
 6.197, p = 0.0009) and for \gls{KSPS} (F(3, 69) = 3.566, p = 0.018). All
 relevant results of the post-hoc tests and the summary of the performance data
 can be observed in Tables \ref{tbl:sum_tkbs_speed} and
-\ref{tbl:res_tkbs_speed}. We further examined, which of the four test keyboard
+\ref{tbl:res_tkbs_speed}. We further examined which of the four test keyboard
 was the fastest for each participant and found, that \textit{Hera} was the
 fastest keyboard in terms of \gls{WPM} for 46\,\% (11) of the twenty-four
 subjects. Additionally, we analyzed the \gls{WPM} percentage of \textit{Own}
 (\gls{OPC}) for all test keyboards to figure out, which keyboard exceeded the
-performance of the participant's own keyboard. We found, that three subjects
+performance of the participant's own keyboard. We found that three subjects
 reached \gls{OPC}\_\gls{WPM} values greater than 100\,\% with all four test
-keyboards. Also, \textit{Athena, Aphrodite} and \textit{Hera} exceeded 100\,\% of
-\gls{OPC}\_\gls{WPM} eight, seven and six times respectively. Detailed results
-are presented in Figure \ref{fig:max_opc_wpm}.
+keyboards. Also, \textit{Athena, Aphrodite} and \textit{Hera} exceeded 100\,\%
+of \gls{OPC}\_\gls{WPM} eight, seven and six times respectively. Detailed
+results are presented in Figure \ref{fig:max_opc_wpm}.
 
 \begin{table}[H]
   \centering
@@ -250,7 +250,7 @@ significant difference. It should be noted, that the 90th percentile of
 \gls{UER} for all keyboards was still below 1\,\%. Summaries for the individual
 metrics and results for all post-hoc tests can be seen in Table
 \ref{tbl:sum_tkbs_err} and \ref{tbl:res_tkbs_err}. Furthermore, we compared the
-\gls{TER} of all test keyboards for each participant and found, that
+\gls{TER} of all test keyboards for each participant and found that
 \textit{Athena} was the keyboard which participants typed most accurately
 with. Two participants scored identical \gls{TER} with two test keyboards,
 therefore the total number of ``1st-placed'' keyboards increased to twenty-six.
@@ -300,7 +300,8 @@ to \textit{Own} (\gls{OPC}). All data can be observed in Figure
     \end{tabular}
   }
   \bottomrule
-  \caption{Summaries for \glsfirst{TER}, \glsfirst{UER} and \glsfirst{CER} for the test keyboards}
+  \caption{Descriptive statistics for \glsfirst{TER}, \glsfirst{UER} and
+    \glsfirst{CER} for the test keyboards}
   \label{tbl:sum_tkbs_err}
 \end{table}
 
@@ -436,9 +437,9 @@ keyboards with a slight exception of \textit{Nyx}, which produced the highest
     \end{tabular}
   }
   \bottomrule
-  \caption{Summaries for the \textit{mean values of} measured muscle activity
-    (\% of \glsfirst{MVC}) in \textit{both typing tests} conducted with each
-    keyboard.}
+  \caption{Descriptive statistics for the \textit{mean values of} measured
+    muscle activity (\% of \glsfirst{MVC}) in \textit{both typing tests}
+    conducted with each keyboard.}
   \label{tbl:sum_tkbs_emg}
 \end{table}
 \pagebreak
@@ -617,8 +618,9 @@ observed in Tables \ref{tbl:res_tkbs_sati} and \ref{tbl:sum_tkbs_sati}.
     Hera      & 63.29 & 70.00  & 12.00 & 92.00 & 19.95 & 4.07 \\
     \bottomrule
   \end{tabular}
-  \caption{Summaries for the additional question \textit{``How satisfied have
-      you been with this keyboard?''} for all four test keyboards}
+  \caption{Descriptive statistics for the additional question \textit{``How
+      satisfied have you been with this keyboard?''} for all four test
+    keyboards}
   \label{tbl:sum_tkbs_sati}
 \end{table}
 
diff --git a/chap7/conclusion.tex b/chap7/conclusion.tex
index 78cd317..68956cc 100644
--- a/chap7/conclusion.tex
+++ b/chap7/conclusion.tex
@@ -11,7 +11,7 @@ specific finger the keyswitch is operated with and hoped to thereby decrease the
 risk for \gls{WRUED}. The evaluation of the impact of different actuation forces
 on typing speed, error rate and satisfaction revealed, that higher actuation
 forces reduce error rates compared to lower actuation forces and that the typing
-speed is also influenced, \textbf{at least indirectly}, by differences in
+speed is also influenced―\textbf{at least indirectly}―by differences in
 actuation force. Especially the keyboard with very low actuation force,
 \textit{Nyx (35\,g)}, which also had the highest average error rate was
 significantly slower than all other keyboards. Therefore, we investigated, if
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