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chap2/literature_review.tex
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% MORE INFO
% WRUED: https://www.ccohs.ca/oshanswers/diseases/rmirsi.html
\section{Literature Review}
% To better understand which metrics and methods are meaningful in the domain of keyboards and especially when
% To investigate whether or not solely the actuation force of individual keys can make a difference in terms of efficiency or satisfaction an ....
\subsection{Work Related Upper Extremity Disorders}
\label{sec:wrued}
\Gls{WRUED} is a term to describe a group of medical conditions related to
muscles, tendons and nerves in shoulder, arm, elbow, forearm or hand, such as
e.g., \gls{CTS}, \gls{RSI}, tendonitis, tension neck syndrome, etc. Symptoms of
\gls{WRUED} are aching, tiredness and fatigue of affected regions that either
occur while working or even extend to phases of relaxation. A common way to
treat \gls{WRUED} is to avoid the potentially harmful activities that cause
discomfort in affected areas \cite{ccfohas_wrued}. Pascarelli and Hsu reported,
that out of 485 patients with \gls{WRUED} 17\% were computer users
\cite{pascarelli_wrued}. Since computers have become an essential part of many
jobs in almost any sector of employment, restrictions of computer related
activities would result in either reduced productivity or the complete inability
to fulfill required tasks, which in the worst case could require a change of
profession \cite{ccfohas_wrued}. Potential problems with current keyboard
designs and possible solutions are discussed in the following sections.
\subsubsection{Relevance for this Thesis}
\gls{WRUED} are a serious problem of modern society and since there is evidence
pointing towards computer related work to be a possible factor for these
diseases, it is likely that especially keyboards, as the main input device, are
responsible for a portion of people affected by \gls{WRUED}.
\subsection{Keyboards and Keyswitches}
\label{sec:kb_ks}
\subsubsection{Keyboard Models and Layouts}
\label{sec:kb_layout}
\begin{figure}[ht]
\centering
\includegraphics[width=1.0\textwidth]{images/keyboard_models.jpg}
\caption{Different keyboard models}
\caption{Different keyboards, including alternative split models, smaller form
factors and traditional layouts such as ISO/IEC 9995 \cite{iso9995-2} and
ANSI-INCITS 154-1988 \cite{ansi-incits-154-1988}}
\label{fig:keyboard_models}
\end{figure}
@ -47,10 +76,10 @@ layout of the keyboard but rather a personal preference. As seen in Figure
\ref{fig:keyboard_models}, there are also non standard physical layouts
available which are often designed to improve the posture of the upper extremity
while typing to reduce the risk of injury or even assist in recovering from
previous \gls{WRUED} \cite{ripat_ergo}. Those designs often split the keyboard
in two halves to reduce ulnar deviation and some designs also allow tenting of
the halves or provide a fixed tent which also reduces forearm pronation
\cite{baker_ergo, rempel_ergo}.
previous \gls{WRUED} \cite{ripat_ergo, tittiranonda_ergo}. Those designs often
split the keyboard in two halves to reduce ulnar deviation and some designs also
allow tenting of the halves or provide a fixed tent which also reduces forearm
pronation \cite{baker_ergo, rempel_ergo}.
\subsubsection{Membrane Keyswitch}
\label{sec:mem_switch}
@ -58,13 +87,13 @@ the halves or provide a fixed tent which also reduces forearm pronation
Besides the exterior design of the keyboard, there is another part of
interest—the keyswitch. This component of a keyboard actually sends the signal
that a key is pressed. There are different types of keyswitches available to
date. The more commonly available ones are scissor switches and rubber dome
switches which are both subsets of the membrane switches. Scissor switches are
often found in keyboards that are integrated into notebooks while rubber dome
switches are mostly used in workplace keyboards. Both variants use a rubber
membrane with small domes underneath each key. When a key is pressed, the
corresponding dome collapses and because the dome's inner wall is coated with a
conductive material, closes an electrical circuit \cite{ergopedia_keyswitch,
date. The most commonly used ones are scissor switches and rubber dome switches
which are both subsets of the membrane switch family. Scissor switches are often
found in keyboards that are integrated into notebooks while rubber dome switches
are mostly used in workplace keyboards. Both variants use a rubber membrane with
small domes underneath each key. When a key is pressed, the corresponding dome
collapses and because the dome's inner wall is coated with a conductive
material, closes an electrical circuit \cite{ergopedia_keyswitch,
peery_3d_keyswitch}.
\subsubsection{Mechanical Keyswitch}
@ -205,7 +234,7 @@ software used and the experimental setup, users have to input different kinds of
text, either for a predefined time or the time is measured till the whole text
is transcribed \cite{chen_typing_test, hoffmann_typeright,
fagarasanu_force_training, akagi_keyswitch, kim_typingforces,
pereira_typing_test}.
pereira_typing_test, baker_ergo}.
\subsubsection{Readability of Text}
\label{sec:meas_fre}
@ -372,6 +401,7 @@ thereby reveal differences that cannot be easily acquired by a device or formula
\subsection{Observer Bias and a Possible Solution}
\label{sec:bias}
As already discussed in Section \ref{sec:metrics}, it is common practice in
research related to typing to present a text that has to be transcribed by the
participant. Usually, the text was chosen by the researcher or already available
@ -410,8 +440,54 @@ mitigate the risk of unwanted bias. In addition, the aspect of time in the
preparation phase of a study could be another factor to consider crowdsourcing
to acquire larger amounts of text with equal difficulty.
\subsection{Influence of Actuation Force on Keyboard use}
\label{sec:finger_force}
Section \ref{sec:kb_ks} discussed the differences of various keyswitch
models. One difference was the applied force, a keyswitch required to
activate. A study by Akagi tested the differences in performance and preference
across four visually identical keyboards with different keyswitches. The
keyswitches differed in actuation force and type. Two keyboards used tactile
keyswitches with 70.9 g (\gls{KB} A) and 32.5 g (\gls{KB} C) the other two
linear switches with 70.9 g (\gls{KB} D) and 42.5 g (\gls{KB} B). The (n=24)
subjects were required to type on each keyboard for 7 to 8 minutes where speed
and errors were recorded. The results showed, that \gls{KB} D (linear, 70.9 g)
produced the lowest error rate followed by \gls{KB} A (tactile, 70.9 g),
\gls{KB} C (linear, 42.5 g) and \gls{KB} B (tactile, 35.5 g). Further, the
difference in typing speed between the slowest (tactile, 70.9 g) and fastest
(linear, 42.5 g) keyboard was only 2.61\% and according to Akagi too small to be
significant in practical use. The study also revealed, that the preference for
neither of the four keyboards was significantly different
\cite{akagi_keyswitch}. A follow up survey by Akagi concerning the model of
keyboard typists would prefer to use in the future revealed, that 69\% of the 81
participating decided for a newly proposed keyboard with 56.7 g resistance and
light tactile feedback \cite{akagi_keyswitch}. Further, a study by Loricchio,
were (n=16) participants typed on two identical keyboard models that only
differed in actuation force (58 g and 74g), also yielded moderate differences in
typing speed. The keyboard with lower actuation force was 8.25\% faster and
preferred by 15 out of the 16 subjects compared to the keyboard featuring
keyswitches with higher actuation force \cite{loricchio_force_speed}. A study by
Hoffmann et al. even designed a keyboard that utilized small
electromagnets―instead of the typically used spring―to dynamically alter the
resistance of keys to prevent erroneous input by increasing the force required
to press keys that do not make sense in the current context of a word. This
design reduced the number of required corrections by 46\% and overall lowered
typos by 87\% compared to when the force feedback was turned off (n=12)
\cite{hoffmann_typeright}.
\subsubsection{Relevance for this Thesis}
So far, studies concerning keyboards with uniform actuation force yielded
different results pertaining speed, but agreed that actuation force influences
the error rate during typing related tasks. To our best knowledge, there are no
studies that evaluated the effect of non-uniformly distributed actuation forces
across one keyboard on speed, accuracy, error rate or preference. This is why we
want to reevaluate the influence of actuation force on speed and determine, if
keyboards with non-uniform actuation forces have a positive impact on all
metrics mentioned so far. The next section gives insights, into why such
keyboards could make sense.
\subsection{Strength of Individual Fingers}
As already mentioned in Section \ref{sec:metrics}, the force applied to a
As already mentioned in Section \ref{sec:mech_switch}, the force applied to a
keyswitch is the concern of multiple studies that evaluate the relation between
keyboarding and \gls{WRUED}. Further, multiple studies came to the conclusion,
that there is a significant discrepancy in strength between individual fingers
@ -438,18 +514,39 @@ all fingers have to apply equal force to generate a keypress because of the
uniform actuation force used in commercially available keyboards.
\subsection{Summary}
Since keyboards are still the most commonly used input method for data entry to
date and so far all efforts to convince the mainstream to move from the
standard, less ergonomic, physical layouts to split keyboards failed, further
alternatives that could be easily implemented into manufacturing processes have
to be explored, to counteract the rising risks for \gls{WRUED}. One factor
related to \gls{WRUED} is the actuation force of the keyswitches
\cite{bufton_typingforces, rempel_ergo, rempel_force,
\label{sec:lr_sum}
Keyboards are still the most commonly used input method for data entry to date
and so far the majority of keyboard users still operates non-alternative
keyboard designs. Thus, modifications that ideally could be implemented into
manufacturing processes of existing designs have to be explored, to ensure
availability and therefore adaption, which could help to reduce the risks of
\gls{WRUED}. One factor related to \gls{WRUED} is the actuation force of the
keyswitches \cite{bufton_typingforces, rempel_ergo, rempel_force,
gerard_keyswitch}. Especially higher actuation forces have shown to be the
reason for discomfort in the upper extremity. On the other hand, higher
actuation forces also led to lower error rates while typing and therefore
enhance user satisfaction and performance \cite{gerard_keyswitch}. With the help
of several methods to measure typing relate metrics such as muscle activity
(\gls{EMG}), error rates (\gls{CER} and \gls{UER}), typing speed (\gls{WPM}) and
user satisfaction {\gls{UEQ} and \gls{KCQ}} it is feasible to evaluate possible
alternative input methods to the more traditional keyboard.
enhance user satisfaction and performance \cite{gerard_keyswitch}. Therefore, a
desirable input method should offer enough resistance to prevent accidental key
presses but also reduce the stress induced on weaker fingers. With the help of
several methods to measure typing relate metrics such as muscle activity
(\gls{EMG}), error rates (\gls{CER} and \gls{UER}), typing speed (\gls{WPM}),
text readability (\gls{FRE}) and user satisfaction (\gls{UEQ} and \gls{KCQ}) it
is feasible to evaluate possible alternative input methods to the more
traditional keyboard. The availability of affordable surface level \gls{EMG}
measurement devices makes it possible for researchers that are not medically
trained to conduct non-invasive muscle activity measurements \cite{takala_emg}
and load cells in combination with micro controllers are a reliable, low-cost
solution to visualize the strength of different fingers and monitor applied
forces while typing \cite{gerard_keyswitch, rempel_ergo,
bufton_typingforces}. Although, the strength of individual fingers has already
been measured in different studies \cite{bretz_finger, martin_force,
baker_kinematics, dickson_finger}, to our best knowledge, there are no
measurements concerning the maximum force each individual finger can apply in
different positions related to a key on the keyboard. Further, during our
research we only found one manufacturer of keyboards (Realforce), that already
offers models with variable actuation force. These keyboards feature two types
of keys and require less force towards the edges and more force towards the
middle \cite{realforce_topre}. We therefore try to provide a sensible
distribution of actuation forces across a non-uniformly equipped keyboard and
evaluate the possible advantages and disadvantages of such a design to encourage
other manufacturers to produce similar alternative keyboard designs.

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chap3/implementation.tex
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@ -1,6 +1,320 @@
\section{Implementation}
For the purpose of this thesis, we programmed our own typing test platform to
have better control over the performance related measurements and the text that
has to be transcribed. Further, the participants had to fill out up to two
questionnaires after each typing test which had to be linked to this specific
typing test or keyboard. With a total number of 24 subjects, five keyboards and
therefore 10 individual typing tests per subject or 240 typing tests in total,
we decided to incorporate a questionnaire feature into our platform to mitigate
the possibility of false mappings between typing tests, surveys and
participants. Additionally, because we wanted to control the understandability
of text without introducing observer bias for the text selection process and
also to save time, we implemented a crowdsourcing feature where individuals
could provide text snippets that were automatically checked for adequate
\gls{FRE}. Finally, we wanted to open source this platform so other researchers
in the field of text entry performance could use it without additional cost.
Another challenge was to measure the maximum force each individual finger is
able to apply to any of the keyswitches on a keyboard. We therefore decided to
prototype a device that is able to simulate the position of different keyswitches
and measure the applied force by the finger usually responsible to actuate a
specific key.
Both implementations are explained in more detail in the following two sections.
\label{sec:label}
\subsection{Typing Test Platform}
\label{sec:label}
The platform we created is called \gls{GoTT} because the backend, which is the
server side code, is programmend in Go, a programming language developed by a
team at Google \cite{golang}. The decision for Go was made, because Go's
standard library offers convenient packages to quickly setup a web server with
simple routing and templating functionalities \cite{golang_std}. The backend and
frontend communicate through a \gls{REST} \gls{API} and exchange data in
\gls{JSON} format. \gls{GoTT} utilizes a document based database to persistently
store login credentials, results of typing tests and all finished
questionnaires. We decided to use \gls{MongoDB} because of the capability to
directly store \gls{JSON}-like, nested, data without prior transformation
\cite{mongodb}. The general functionality of \gls{GoTT} can be seen in Figure
\ref{fig:gott_arch}.
\begin{figure}[H]
\centering
\includegraphics[width=1.0\textwidth]{images/gott_arch.png}
\caption{Overview of the general functionality of \gls{GoTT}}
\label{fig:gott_arch}
\end{figure}
The platform offers three major functionalities that are important for this thesis:
\begin{enumerate}
\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
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
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}.
\begin{figure}[ht]
\centering
\includegraphics[width=1.0\textwidth]{images/gott_colorblind.png}
\caption{\gls{GoTT}'s text area perceived with different kinds of
colorblindness. The examples are ordered from top, impairments most
commonly found in the population, to bottom (least common) and are
simulated with the tool \textit{Color Oracle} \cite{colororacle}}
\label{fig:gott_colorblind}
\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
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
discomfort and wants to stop. The timer for the typing test starts when the
participant inputs the first character after the start button was pressed. The
\gls{UI} for the typing test is shown in Figure \ref{fig:gott_text_area}.
\begin{figure}[ht]
\centering
\includegraphics[width=1.0\textwidth]{images/gott_text_area.jpg}
\caption{\gls{GoTT}'s typing test. The \textit{START} button reveals the
text selected with the dropdown menu labeled \textit{Text to
transcribe}. The \textit{RESET} button interrupts the currently active
typing test. The content will scroll up one line after half of the text
was transcribed (Marked by \textit{Scrolling begins here}) so the relevant
line always stays centered.}
\label{fig:gott_text_area}
\end{figure}
\gls{GoTT} captures the metrics presented in Listing \ref{lst:meas_perf}
according to the formulas given in Section \ref{sec:meas_perf}:
\begin{listing}[H]
\caption{Implementation of performance related metrics in \gls{GoTT}.
The function \textit{roundToPrecision} takes the number of decimal places
to round to as the second argument.}
\label{lst:meas_perf}
\begin{minted}[linenos]{js}
// TEST_TIME is retrieved from backend and
// set in the config file in seconds
mins = TEST_TIME / 60;
// T is the transcribed text
TL = T.length;
// Input Stream Length = TL + Fixes (Backspace)
// + Incorrect Fixed (Fixed Errors)
ISL = TL + F + IF;
// Correct input = TL - Incorrect Not Fixed (Left errors)
C = TL - INF;
// Error metrics
CER = roundToPrecision(IF / (TL + IF), 5);
UER = roundToPrecision(INF / (TL + IF), 5);
TER = roundToPrecision((INF + IF)/(TL + IF), 5);
KSPC = roundToPrecision(ISL / TL, 5);
// Correct / Any input char
accuracy = roundToPrecision(C / (TL + IF) * 100, 2);
// Speed metrics
// TL - 1 because the first char is entered at 0 seconds
WPM = roundToPrecision((TL - 1) / (5 * mins), 2);
AdjWPM = roundToPrecision(WPM * Math.pow((1 - UER), a), 2);
KSPS = roundToPrecision((ISL - 1) / TEST_TIME, 5);
\end{minted}
\end{listing}
For further implementation details on how input was captured or sent to the
backend refer to the code in the online repository \footnote{TODO: GITHUB}.
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.
\item \textbf{The questionnaires} had to be linked to a specific participant,
typing test and keyboard. In total, three different types of questionnaires had
to be filled out by each participant at different times (more information in
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. 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
\ref{sec:bias}. Further, this part of \gls{GoTT} helped us gather 44639 instead
of the estimated 40000 required characters to provide enough text for ten
non-overlapping texts. The goal was reached after only 2 days, which proved
crowdsourcing to be a good method to efficiently gather greater amounts of
text for our experiment. The estimation of 40000 characters was made according
to Eq. \ref{eq:chars}.
\begin{equation}
\label{eq:chars}
n_{kb} * m_{ttkb} * \frac{s}{60} * |w| * wpm_{max} = 5 * 2 * \frac{300}{60} * 5 * 160 = 40000
\end{equation}
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}}
which included the top 25000 competitors in terms of average \gls{WPM}
\cite{typeracer}.
The text snippets provided by volunteers trough our platform had to fulfill three
requirements:
\begin{enumerate}
\item German language
\item Fairly easy to understand (\gls{FRE} $>$ 70
\cite{flesch_fre})
\item Number of characters must be between 200 and 300
\end{enumerate}
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
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}
utilizes regex \footnote{\url{https://github.com/google/re2/wiki/Syntax}}
matching to identify the number of syllables in a given string in German
language. The rules for hyphenation defined by Duden online
\footnote{\url{https://www.duden.de/sprachwissen/rechtschreibregeln/worttrennung}}
were used to derive the regex patterns to identify syllables
\cite{duden_hyphen}. The \gls{FRE} scores yielded by our function were verified
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}.
\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.}
\label{lst:gott_fre}
\begin{minted}[linenos]{go}
func countSyllables(txt string) int {
rx := regexp.MustCompile(`(?i)[^aeiouäöüßy\W][aeiouäöüßy]|
\b[aeiouäöüßy][^aeiouäöüßy\W]|\b[aeiouäöüy]{2,}|
u[aeuo]|(on|er)\b|\B(a|o|u|e)\B`)
extraConsonants := []string{"ck", "x", "ch", "x", "sch", "x",
"st", "x", "gn", "x"}
extraVowels := []string{"äu", "i", "ie", "i"}
r := strings.NewReplacer(extraConsonants...)
txt = r.Replace(txt)
r = strings.NewReplacer(extraVowels...)
txt = r.Replace(txt)
syllableCount := len(rx.FindAllStringIndex(txt, -1))
return syllableCount
}
func countWords(txt string) int {
rx := regexp.MustCompile(`[\wäöüß]{2,}`)
return len(rx.FindAllStringIndex(txt, -1))
}
func countSentences(txt string) int {
rx := regexp.MustCompile(`[\wäöüß]{2,}[\?\.!;]`)
return len(rx.FindAllStringIndex(txt, -1))
}
// Flesch-Reading-Ease (German)
// FRE = 180 - ASL - (58.5 * ASW)
// ASL = Average Sentence Length = Words / Sentence
// ASW = Average Number of Syllables per Word = Syllables / Words
func calculateFRE(txt string) float64 {
syc := countSyllables(txt)
wc := countWords(txt)
sec := countSentences(txt)
asl := float64(wc) / float64(sec)
asw := float64(syc) / float64(wc)
fre := math.Round((180.-asl-(58.5*asw))*100) / 100
// <0 and >100 is allowed, though not relevant in this case
if fre > 100. { fre = 100. }
if fre < 0. { fre = 0. }
return fre
}
\end{minted}
\end{listing}
\end{enumerate}
\subsection{Finger strength measurement device}
\label{sec:label}
\label{sec:force_meas_dev}
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\textwidth]{images/force_master_1}
\caption{Prototype of a measuring device that simulates the distance and finger position required to press different keys on a keyboard. The display shows the currently applied force in gram and the peak force applied throughout the current measurement in gram and \gls{N}}
\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 wrest 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
\includegraphics[width=0.5\textwidth]{images/hx711}
\caption{HX711 amplifier module. The 0 $\Omega$ resistor had to be resoldered
to accomplish 80 Hz polling rate. This module is used in combination with
the HX711\_ADC library to read the changes in resistance by the load cell
and convert those into gram.}
\label{fig:hx711}
\end{figure}
\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,
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
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
peak force will be addressed in the following section.

107
chap4/methodology.tex
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@ -1,6 +1,109 @@
\section{Methodology}
\label{sec:methodology}
\subsection{Research Approach}
\subsection{Market analysis of available mechanical keyswitches}
Because of the controversial findings about the impact of key actuation forces
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
actuation forces on speed, error rate and satisfaction. To our best knowledge,
to this date, there is no published work about the influence of a keyboard with
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, to values obtained with the participant's own
keyboards.
\subsection{Preliminary telephone interview}
Some of the studies we found that researched implications of actuation force on
speed, preference or other metrics were published between 1984 and 2010. That is
why we wanted to ascertain if and how, with the advance of technology in recent
years and especially the capabilities modern smartphones offer, keyboard usage
has changed. Further, we wanted to gather information about the preference of
key resistance, keyswitch type and experiences with \gls{WRUED}. Therefore, we
conducted a structured interview with seventeen volunteers (59\% females) via
telephone. The age of the subjects ranged between 22 and 52 with a mean age of
29 years. The professions of subjects were distributed among medical workers,
students, office employees, computer engineers and community workers. The first
question we asked was \textit{``Which keyboard in terms of actuation force would
be the most satisfying for you to use in the long run?''}. Thirteen (76\%) out
of the seventeen subjects mentioned, that they would prefer a keyboard with
light actuation force over a keyboard with higher resistance. The next question
\textit{``Have you ever had pain when using a keyboard and if so, where did you
have pain?''} yielded, that 41\% of those polled experienced pain at least
once while using a keyboard. The areas affected described by the seven who
already experienced pain were the wrist \underline{and} forearm (3 out of 7),
wrist only (2 out of 7), fingers (1 out of 7) and forearm only (1 out of 7). The
results for the third question \textit{``Which keyboard are you currently using
and for how many hours a day on average?''} were in line with the statements
we found during our literature 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 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 \cite{mikkelsen_duration}. The last question \textit{``Which
tasks do you still prefer to perform with a keyboard rather than your mobile
phone?''} revealed, that all of the subjects preferred to use a keyboard when
entering greater amounts of data (emails, applications, presentations,
calculations, research), but also surprisingly 41\% preferred to use a keyboard
to write instant messages (chatting via Whatsapp
Web\footnote{\url{https://web.whatsapp.com/}}, Signal
Desktop\footnote{\url{https://signal.org/download/}}, Telegram
Desktop\footnote{\url{https://desktop.telegram.org/}}).
\subsection{Market analysis of available mechanical keyswitches}
To gather information about available actuation forces, we collected the product
lines of keyswitches for all well known manufacturers, namely
Cherry\footnote{\url{https://www.cherrymx.de/en/mx-original/mx-red.html}},
Kailh\footnote{\url{https://www.kailhswitch.com/mechanical-keyboard-switches/}},
Gateron\footnote{\url{http://www.gateron.com/col/58459?lang=en}},
Matias\footnote{\url{http://matias.ca/switches/}},
Razer\footnote{\url{https://www.razer.com/razer-mechanical-switches}} and
Logitech\footnote{\url{https://www.logitechg.com/en-us/innovation/mechanical-switches.html}}. Since
some of the key actuation forces listed on the manufacturers or resellers
websites were given in cN and most of them in g or gf, the values were adjusted
to gram to reflect a trend that is within a margin of ± 2 g of accuracy. The
results shown in Figure \ref{fig:keyswitches_brands} are used to determine the
minimum, maximum and most common actuation force for broadly available
keyswitches. According to our findings, the lowest commercially available
actuation force is 35 g ($\approx$ 0.34 \gls{N}) the most common one is 50 g
($\approx$ 0.49 \gls{N}) and the highest resistance available is 80 g ($\approx$
0.78 \gls{N}).
\begin{figure}[ht]
\centering
\includegraphics[width=1.0\textwidth]{images/keyswitches_brands}
\caption{Available actuation forces for keyswitches of major keyswitch manufacturers}
\label{fig:keyswitches_brands}
\end{figure}
\subsection{Preliminary study of finger strength}
% armstrong measurments of finger strength
To evaluate the impact of an adjusted keyboard (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 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 differences in applicable
force depending on the position a finger has to enter to press a certain key.
To detect possible differences in peak force depending on the position of the
fingers, we conducted an experiment with six volunteers (50\%
females). Subject's ages ranged from 20 to 26 with a mean age of 24 years. The
subjects were all personal contacts. Subjects professions were 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}. The order of
positions in which the participants had to press the key was complete counterbalanced

16
glossary.tex
View File

@ -24,8 +24,15 @@
\newacronym{KSPC}{KSPC}{Keystrokes per Character}
\newacronym{UEQ-S}{UEQ-S}{short version of the user experience questionnaire}
\newacronym{UEQ}{UEQ}{user experience questionnaire}
\newacronym{KCQ}{KCQ}{keyboard comfort questionnaire}
\newacronym{GoTT}{GoTT}{Go Typing Test}
\newacronym{API}{API}{application programming interface}
\newacronym{REST}{REST}{representational state transfer}
\newacronym{JSON}{JSON}{javascript object notation}
\newacronym{UI}{UI}{user interface}
\newacronym{PTTQ}{PTTQ}{post typing test questionnaire}
\newacronym{PKQ}{PKQ}{post keyboard questionnaire}
\newacronym{OLED}{OLED}{organic light-emitting diode}
\newglossaryentry{N}{
name={N},
@ -62,3 +69,8 @@ description={Describes the scenario when the typist does not release the key bef
name={Topre},
description={Topre switches are keyswitches produced by the Japanese company Topre Corporation}
}
\newglossaryentry{MongoDB}{
name={MongoDB},
description={General purpose, document-based database which name originates from the word humongous}
}

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97
ref_shelf.bib
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@ -332,6 +332,17 @@ urldate = {2021-06-28}
publisher = {American Occupational Therapy Association}
}
@article{tittiranonda_ergo,
title={Effect of four computer keyboards in computer users with upper extremity musculoskeletal disorders},
author={Tittiranonda, Pat and Rempel, David and Armstrong, Thomas and Burastero, Stephen},
journal={American journal of industrial medicine},
volume={35},
number={6},
pages={647--661},
year={1999},
publisher={Wiley Online Library}
}
@article{rempel_ergo,
title = {The effect of six keyboard designs on wrist and forearm
postures},
@ -711,12 +722,14 @@ urldate = {2021-07-01}
year={2017}
}
@article{schenk_crowdsource,
author = {Schenk, Eric and Guittard, Claude},
year = {2009},
month = {01},
pages = {},
title = {Crowdsourcing: What can be Outsourced to the Crowd, and Why ?}
@inproceedings{schenk_crowdsource,
title={Crowdsourcing: What can be Outsourced to the Crowd, and Why},
author={Schenk, Eric and Guittard, Claude and others},
booktitle={Workshop on open source innovation, Strasbourg, France},
volume={72},
pages={3},
year={2009},
organization={Citeseer}
}
@article{howe_crowdsource,
@ -777,3 +790,75 @@ title = {Crowdsourcing: What can be Outsourced to the Crowd, and Why ?}
year={1972},
publisher={Elsevier}
}
@online{ccfohas_wrued,
author = {Canadian Centre for Occupational Health and Safety},
title = {Work-related Musculoskeletal Disorders (WMSDs)},
url = {https://www.ccohs.ca/oshanswers/diseases/rmirsi.html},
urldate = {2021-07-05}
}
@article{pascarelli_wrued,
title={Understanding work-related upper extremity disorders: clinical findings in 485 computer users, musicians, and others},
author={Pascarelli, Emil F and Hsu, Yu-Pin},
journal={Journal of Occupational Rehabilitation},
volume={11},
number={1},
pages={1--21},
year={2001},
publisher={Springer}
}
@online{golang,
author = {Google},
title = {The Go Project},
url = {https://golang.org/project},
urldate = {2021-07-05}
}
@online{golang_std,
author = {Google},
title = {Go standard library},
url = {https://golang.org/pkg/},
urldate = {2021-07-05}
}
@online{mongodb,
author = {MongoDB Inc.},
title = {The document model},
url = {https://www.mongodb.com/},
urldate = {2021-07-05}
}
@online{colororacle,
author = {Bernie Jenny},
title = {Color Oracle - Usage & Forms of Color Vision Impairment},
url = {https://colororacle.org/usage.html},
urldate = {2021-07-06}
}
@online{typeracer,
author = {The TypeRacer Blog},
title = {2018 Competition Results – Are You In The Top-25,000?},
url = {https://typeracer.wordpress.com/2019/01/27/2018-competition-results-are-you-in-the-top-25000/},
urldate = {2021-07-06}
}
@online{duden_hyphen,
author = {Dudenredaktion (o. J.)},
title = {Worttrennung},
url = {https://www.duden.de/sprachwissen/rechtschreibregeln/worttrennung},
urldate = {2021-07-06}
}
@article{mikkelsen_duration,
title={Validity of questionnaire self-reports on computer, mouse and keyboard usage during a four-week period},
author={Mikkelsen, Sigurd and Vilstrup, Imogen and Lassen, Christina Funch and Kryger, Ann Isabel and Thomsen, Jane Fr{\o}lund and Andersen, Johan Hviid},
journal={Occupational and Environmental Medicine},
volume={64},
number={8},
pages={541--547},
year={2007},
publisher={BMJ Publishing Group Ltd}
}

8
thesis.tex
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@ -19,6 +19,10 @@
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{kpfonts}
\usepackage[outputdir=auto]{minted}
\usepackage{mdframed}
\BeforeBeginEnvironment{minted}{\begin{mdframed}}
\AfterEndEnvironment{minted}{\end{mdframed}}
% \usepackage{mathpazo}
% verbesserter Randausgleich
@ -135,8 +139,8 @@
\cleardoublepage
%Kurfassung/Abstract German (only for thesis written in German)
% \include{abstractDE}
% \cleardoublepage
\include{abstractDE}
\cleardoublepage
%Kurzfassung/Abstract Englisch (for every thesis)
\include{abstractEN}