update: cleanup and corrections

master
phga 4 years ago
parent a2aaad9924
commit 1e87867321

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@ -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}

@ -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.

@ -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}

@ -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}

@ -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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