\documentclass[twoside,openright]{uva-bachelor-thesis}

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% Title Page
\title{A generic architecture for gesture-based interaction}
\author{Taddeüs Kroes}
\supervisors{Dr. Robert G. Belleman (UvA)}
\signedby{Dr. Robert G. Belleman (UvA)}

\begin{document}

% Title page
\maketitle
\begin{abstract}
    Applications that use complex gesture-based interaction need to translate
    primitive messages from low-level device drivers to complex, high-level
    gestures, and map these gestures to elements in an application. This report
    presents a generic architecture for the detection of complex gestures in an
    application. The architecture translates device driver messages to a common
    set of ``events''. The events are then delegated to a tree of ``event
    areas'', which are used to separate groups of events and assign these
    groups to an element in the application. Gesture detection is performed on
    a group of events assigned to an event area, using detection units called
    ``gesture tackers''. An implementation of the architecture as a daemon
    process would be capable of serving gestures to multiple applications at
    the same time. A reference implementation and two test case applications
    have been created to test the effectiveness of the architecture design.
    % TODO: conclusions
\end{abstract}

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\chapter{Introduction}
\label{chapter:introduction}

Surface-touch devices have evolved from pen-based tablets to single-touch
trackpads, to multi-touch devices like smartphones and tablets. Multi-touch
devices enable a user to interact with software using hand gestures, making the
interaction more expressive and intuitive. These gestures are more complex than
primitive ``click'' or ``tap'' events that are used by single-touch devices.
Some examples of more complex gestures are ``pinch''\footnote{A ``pinch''
gesture is formed by performing a pinching movement with multiple fingers on a
multi-touch surface. Pinch gestures are often used to zoom in or out on an
object.} and ``flick''\footnote{A ``flick'' gesture is the act of grabbing an
object and throwing it in a direction on a touch surface, giving it momentum to
move for some time after the hand releases the surface.} gestures.

The complexity of gestures is not limited to navigation in smartphones. Some
multi-touch devices are already capable of recognizing objects touching the
screen \cite[Microsoft Surface]{mssurface}. In the near future, touch screens
will possibly be extended or even replaced with in-air interaction (Microsoft's
Kinect \cite{kinect} and the Leap \cite{leap}).

The interaction devices mentioned above generate primitive events. In the case
of surface-touch devices, these are \emph{down}, \emph{move} and \emph{up}
events. Application programmers who want to incorporate complex, intuitive
gestures in their application face the challenge of interpreting these
primitive events as gestures. With the increasing complexity of gestures, the
complexity of the logic required to detect these gestures increases as well.
This challenge limits, or even deters the application developer to use complex
gestures in an application.

The main question in this research project is whether a generic architecture
for the detection of complex interaction gestures can be designed, with the
capability of managing the complexity of gesture detection logic. The ultimate
goal would be to create an implementation of this architecture that can be
extended to support a wide range of complex gestures. With the existence of
such an implementation, application developers do not need to reinvent gesture
detection for every new gesture-based application.

    \section{Contents of this document}

    The scope of this thesis is limited to the detection of gestures on
    multi-touch surface devices. It presents a design for a generic gesture
    detection architecture for use in multi-touch based applications. A
    reference implementation of this design is used in some test case
    applications, whose purpose is to test the effectiveness of the design and
    detect its shortcomings.

    Chapter \ref{chapter:related} describes related work that inspired a design
    for the architecture. The design is presented in chapter
    \ref{chapter:design}. Chapter \ref{chapter:testapps} presents a reference
    implementation of the architecture, and two test case applications that
    show the practical use of its components as presented in chapter
    \ref{chapter:design}. Finally, some suggestions for future research on the
    subject are given in chapter \ref{chapter:futurework}.

\chapter{Related work}
\label{chapter:related}

    Applications that use gesture-based interaction need a graphical user
    interface (GUI) on which gestures can be performed. The creation of a GUI
    is a platform-specific task. For instance, Windows and Linux support
    different window managers. To create a window in a platform-independent
    application, the application would need to include separate functionalities
    for supported platforms. For this reason, GUI-based applications are often
    built on top of an application framework that abstracts platform-specific
    tasks. Frameworks often include a set of tools and events that help the
    developer to easily build advanced GUI widgets.

    % Existing frameworks (and why they're not good enough)
    Some frameworks, such as Nokia's Qt \cite{qt}, provide support for basic
    multi-touch gestures like tapping, rotation or pinching. However, the
    detection of gestures is embedded in the framework code in an inseparable
    way. Consequently, an application developer who wants to use multi-touch
    interaction in an application, is forced to use an application framework
    that includes support for those multi-touch gestures that are required by
    the application. Kivy \cite{kivy} is a GUI framework for Python
    applications, with support for multi-touch gestures. It uses a basic
    gesture detection algorithm that allows developers to define custom
    gestures to some degree \cite{kivygesture} using a set of touch point
    coordinates. However, these frameworks do not provide support for extension
    with custom complex gestures.

    Many frameworks are also device-specific, meaning that they are developed
    for use on either a tablet, smartphone, PC or other device. OpenNI
    \cite{OpenNI2010}, for example, provides API's for only natural interaction
    (NI) devices such as webcams and microphones. The concept of complex
    gesture-based interaction, however, is applicable to a much wider set of
    devices. VRPN \cite{VRPN} provides a software library that abstracts the
    output of devices, which enables it to support a wide set of devices used
    in Virtual Reality (VR) interaction. The framework makes the low-level
    events of these devices accessible in a client application using network
    communication. Gesture detection is not included in VRPN.

    % Methods of gesture detection
    The detection of high-level gestures from low-level events can be
    approached in several ways. GART \cite{GART} is a toolkit for the
    development of gesture-based applications, which states that the best way
    to classify gestures is to use machine learning. The programmer trains an
    application to recognize gestures using a machine learning library from the
    toolkit. Though multi-touch input is not directly supported by the toolkit,
    the level of abstraction does allow for it to be implemented in the form of
    a ``touch'' sensor. The reason to use machine learning is that gesture
    detection ``is likely to become increasingly complex and unmanageable''
    when using a predefined set of rules to detect whether some sensor input
    can be classified as a specific gesture.

    The alternative to machine learning is to define a predefined set of rules
    for each gesture. Manoj Kumar \cite{win7touch} presents a Windows 7
    application, written in Microsofts .NET, which detects a set of basic
    directional gestures based on the movement of a stylus. The complexity of
    the code is managed by the separation of different gesture types in
    different detection units called ``gesture trackers''. The application
    shows that predefined gesture detection rules do not necessarily produce
    unmanageable code.

    \section{Analysis of related work}

    Implementations for the support of complex gesture based interaction do
    already exist. However, gesture detection in these implementations is
    device-specific (Nokia Qt and OpenNI) or limited to use within an
    application framework (Kivy).

    An abstraction of device output allows VRPN and GART to support multiple
    devices. However, VRPN does not incorporate gesture detection. GART does,
    but only in the form of machine learning algorithms. Many applications for
    mobile phones and tablets only use simple gestures such as taps. For this
    category of applications, machine learning is an excessively complex method
    of gesture detection. Manoj Kumar shows that when managed well, a
    predefined set of gesture detection rules is sufficient to detect simple
    gestures.

    This thesis explores the possibility to create an architecture that
    combines support for multiple input devices with different methods of
    gesture detection.

\chapter{Design}
\label{chapter:design}

    % Diagrams are defined in a separate file
    \input{data/diagrams}

    \section{Introduction}

    Application frameworks are a necessity when it comes to fast,
    cross-platform development. A generic architecture design should aim to be
    compatible with existing frameworks, and provide a way to detect and extend
    gestures independent of the framework. Since an application framework is
    written in a specific programming language, the architecture should be
    accessible for applications using a language-independent method of
    communication. This intention leads towards the concept of a dedicated
    gesture detection application that serves gestures to multiple applications
    at the same time.

    This chapter describes a design for such an architecture. The architecture
    components are shown by figure \ref{fig:fulldiagram}. Sections
    \ref{sec:multipledrivers} to \ref{sec:daemon} explain the use of all
    components in detail.

    \fulldiagram
    \newpage

    \section{Supporting multiple drivers}
    \label{sec:multipledrivers}

    The TUIO protocol \cite{TUIO} is an example of a driver that can be used by
    multi-touch devices. TUIO uses ALIVE- and SET-messages to communicate
    low-level touch events (see appendix \ref{app:tuio} for more details).
    These messages are specific to the API of the TUIO protocol. Other drivers
    may use different messages types. To support more than one driver in the
    architecture, there must be some translation from device-specific messages
    to a common format for primitive touch events.  After all, the gesture
    detection logic in a ``generic'' architecture should not be implemented
    based on device-specific messages. The event types in this format should be
    chosen so that multiple drivers can trigger the same events. If each
    supported driver would add its own set of event types to the common format,
    the purpose of it being ``common'' would be defeated.

    A minimal expectation for a touch device driver is that it detects simple
    touch points, with a ``point'' being an object at an $(x, y)$ position on
    the touch surface. This yields a basic set of events: $\{point\_down,
    point\_move, point\_up\}$.

    The TUIO protocol supports fiducials\footnote{A fiducial is a pattern used
    by some touch devices to identify objects.}, which also have a rotational
    property. This results in a more extended set: $\{point\_down, point\_move,
    point\_up, object\_down, object\_move, object\_up,\\ object\_rotate\}$.
    Due to their generic nature, the use of these events is not limited to the
    TUIO protocol. Another driver that can keep apart rotated objects from
    simple touch points could also trigger them.

    The component that translates device-specific messages to common events, is
    called the \emph{event driver}. The event driver runs in a loop, receiving
    and analyzing driver messages. When a sequence of messages is analyzed as
    an event, the event driver delegates the event to other components in the
    architecture for translation to gestures.

    Support for a touch driver can be added by adding an event driver
    implementation. The choice of event driver implementation that is used in an
    application is dependent on the driver support of the touch device being
    used.

    Because event driver implementations have a common output format in the
    form of events, multiple event drivers can be used at the same time (see
    figure \ref{fig:multipledrivers}). This design feature allows low-level
    events from multiple devices to be aggregated into high-level gestures.

    \multipledriversdiagram

    \section{Event areas: connecting gesture events to widgets}
    \label{sec:areas}

    Touch input devices are unaware of the graphical input
    widgets\footnote{``Widget'' is a name commonly used to identify an element
    of a graphical user interface (GUI).} rendered by an application, and
    therefore generate events that simply identify the screen location at which
    an event takes place. User interfaces of applications that do not run in
    full screen modus are contained in a window. Events which occur outside the
    application window should not be handled by the application in most cases.
    What's more, a widget within the application window itself should be able
    to respond to different gestures. E.g. a button widget may respond to a
    ``tap'' gesture to be activated, whereas the application window responds to
    a ``pinch'' gesture to be resized. In order to restrict the occurence of a
    gesture to a particular widget in an application, the events used for the
    gesture must be restricted to the area of the screen covered by that
    widget. An important question is if the architecture should offer a
    solution to this problem, or leave the task of assigning gestures to
    application widgets to the application developer.

    If the architecture does not provide a solution, the ``gesture detection''
    component in figure \ref{fig:fulldiagram} receives all events that occur on
    the screen surface. The gesture detection logic thus uses all events as
    input to detect a gesture. This leaves no possibility for a gesture to
    occur at multiple screen positions at the same time. The problem is
    illustrated in figure \ref{fig:ex1}, where two widgets on the screen can be
    rotated independently. The rotation detection component that detects
    rotation gestures receives all four fingers as input. If the two groups of
    finger events are not separated by clustering them based on the area in
    which they are placed, only one rotation event will occur.

    \examplefigureone

    A gesture detection component could perform a heuristic way of clustering
    based on the distance between events. However, this method cannot guarantee
    that a cluster of events corresponds with a particular application widget.
    In short, a gesture detection component is difficult to implement without
    awareness of the location of application widgets.  Secondly, the
    application developer still needs to direct gestures to a particular widget
    manually. This requires geometric calculations in the application logic,
    which is a tedious and error-prone task for the developer.

    The architecture described here groups events that occur inside the area
    covered by a widget, before passing them on to a gesture detection
    component.  Different gesture detection components can then detect gestures
    simultaneously, based on different sets of input events. An area of the
    screen surface is represented by an \emph{event area}. An event area
    filters input events based on their location, and then delegates events to
    gesture detection components that are assigned to the event area. Events
    which are located outside the event area are not delegated to its gesture
    detection components.

    In the example of figure \ref{fig:ex1}, the two rotatable widgets can be
    represented by two event areas, each having a different rotation detection
    component. Each event area can consist of four corner locations of the
    square it represents. To detect whether an event is located inside a
    square, the event areas use a point-in-polygon (PIP) test \cite{PIP}. It is
    the task of the client application to update the corner locations of the
    event area with those of the widget.

    \subsection{Callback mechanism}

    When a gesture is detected by a gesture detection component, it must be
    handled by the client application. A common way to handle events in an
    application is a ``callback'' mechanism: the application developer binds a
    function to an event, that is called when the event occurs. Because of the
    familiarity of this concept with developers, the architecture uses a
    callback mechanism to handle gestures in an application. Callback handlers
    are bound to event areas, since event areas control the grouping of events
    and thus the occurrence of gestures in an area of the screen.

    \subsection{Area tree}
    \label{sec:tree}

    A basic data structure of event areas in the architecture would be a list
    of event areas. When the event driver delegates an event, it is accepted by
    each event area that contains the event coordinates.

    If the architecture were to be used in combination with an application
    framework, each widget that responds to gestures should have a mirroring
    event area that synchronizes its location with that of the widget. Consider
    a panel with five buttons that all listen to a ``tap'' event. If the
    location of the panel changes as a result of movement of the application
    window, the positions of all buttons have to be updated too.

    This process is simplified by the arrangement of event areas in a tree
    structure. A root event area represents the panel, containing five other
    event areas which are positioned relative to the root area. The relative
    positions do not need to be updated when the panel area changes its
    position. GUI frameworks use this kind of tree structure to manage
    graphical widgets.

    If the GUI toolkit provides an API for requesting the position and size of
    a widget, a recommended first step when developing an application is to
    create a subclass of the area that automatically synchronizes with the
    position of a widget from the GUI framework. For example, the test
    application described in section \ref{sec:testapp} extends the GTK
    \cite{GTK} application window widget with the functionality of a
    rectangular event area, to direct touch events to an application window.

    \subsection{Event propagation}
    \label{sec:eventpropagation}

    Another problem occurs when event areas overlap, as shown by figure
    \ref{fig:eventpropagation}. When the white square is dragged, the gray
    square should stay at its current position. This means that events that are
    used for dragging of the white square, should not be used for dragging of
    the gray square. The use of event areas alone does not provide a solution
    here, since both the gray and the white event area accept an event that
    occurs within the white square.

    The problem described above is a common problem in GUI applications, and
    there is a common solution (used by GTK \cite{gtkeventpropagation}, among
    others). An event is passed to an ``event handler''. If the handler returns
    \texttt{true}, the event is considered ``handled'' and is not
    ``propagated'' to other widgets. Applied to the example of the draggable
    squares, the rotation detection component of the white square should stop
    the propagation of events to the event area of the gray square.

    In the example, rotation of the white square has priority over rotation of
    the gray square because the white area is the widget actually being touched
    at the screen surface. In general, events should be delegated to event
    areas according to the order in which the event areas are positioned over
    each other. The tree structure in which event areas are arranged, is an
    ideal tool to determine the order in which an event is delegated.  An
    object touching the screen is essentially touching the deepest event area
    in the tree that contains the triggered event, which must be the first to
    receive the event. When the gesture trackers of the event area are
    finished with the event, it is propagated to the siblings and parent in the
    event area tree. Optionally, a gesture tracker can stop the propagation of
    the event by its corresponding event area. Figure
    \ref{fig:eventpropagation} demonstrates event propagation in the example of
    the draggable squares.

    \eventpropagationfigure

    An additional type of event propagation is ``immediate propagation'', which
    indicates propagation of an event from one gesture detection component to
    another. This is applicable when an event area uses more than one gesture
    detection component. When regular propagation is stopped, the event is
    propagated to other gesture detection components first, before actually
    being stopped. One of the components can also stop the immediate
    propagation of an event, so that the event is not passed to the next
    gesture detection component, nor to the ancestors of the event area.

    The concept of an event area is based on the assumption that the set of
    originating events that form a particular gesture, can be determined
    exclusively based on the location of the events.  This is a reasonable
    assumption for simple touch objects whose only parameter is a position,
    such as a pen or a human finger. However, more complex touch objects can
    have additional parameters, such as rotational orientation or color. An
    even more generic concept is the \emph{event filter}, which detects whether
    an event should be assigned to a particular gesture detection component
    based on all available parameters. This level of abstraction provides
    additional methods of interaction. For example, a camera-based multi-touch
    surface could make a distinction between gestures performed with a blue
    gloved hand, and gestures performed with a green gloved hand.

    As mentioned in the introduction chapter [\ref{chapter:introduction}], the
    scope of this thesis is limited to multi-touch surface based devices, for
    which the \emph{event area} concept suffices. Section \ref{sec:eventfilter}
    explores the possibility of event areas to be replaced with event filters.

    \section{Detecting gestures from low-level events}
    \label{sec:gesture-detection}

    The low-level events that are grouped by an event area must be translated
    to high-level gestures in some way. Simple gestures, such as a tap or the
    dragging of an element using one finger, are easy to detect by comparing
    the positions of sequential $point\_down$ and $point\_move$ events. More
    complex gestures, like the writing of a character from the alphabet,
    require more advanced detection algorithms.

    Sequences of events that are triggered by a multi-touch based surfaces are
    often of a manageable complexity. An imperative programming style is
    sufficient to detect many common gestures, like rotation and dragging. The
    imperative programming style is also familiar and understandable for a wide
    range of application developers. Therefore, the architecture should support
    this style of gesture detection. A problem with an imperative programming
    style is that the explicit detection of different gestures requires
    different gesture detection components. If these components are not managed
    well, the detection logic is prone to become chaotic and over-complex.

    A way to detect more complex gestures based on a sequence of input events,
    is with the use of machine learning methods, such as the Hidden Markov
    Models (HMM)\footnote{A Hidden Markov Model (HMM) is a statistical model
    without a memory, it can be used to detect gestures based on the current
    input state alone.} used for sign language detection by Gerhard Rigoll et
    al.  \cite{conf/gw/RigollKE97}. A sequence of input states can be mapped to
    a feature vector that is recognized as a particular gesture with a certain
    probability. An advantage of using machine learning with respect to an
    imperative programming style, is that complex gestures are described
    without the use of explicit detection logic, thus reducing code complexity.
    For example, the detection of the character `A' being written on the screen
    is difficult to implement using explicit detection code, whereas a trained
    machine learning system can produce a match with relative ease.

    To manage complexity and support multiple styles of gesture detection
    logic, the architecture has adopted the tracker-based design as described
    by Manoj Kumar \cite{win7touch}. Different detection components are wrapped
    in separate gesture tracking units called \emph{gesture trackers}. The
    input of a gesture tracker is provided by an event area in the form of
    events.  Each gesture detection component is wrapped in a gesture tracker
    with a fixed type of input and output. Internally, the gesture tracker can
    adopt any programming style. A character recognition component can use an
    HMM, whereas a tap detection component defines a simple function that
    compares event coordinates.

    When a gesture tracker detects a gesture, this gesture is triggered in the
    corresponding event area. The event area then calls the callbacks which are
    bound to the gesture type by the application.

    The use of gesture trackers as small detection units allows extendability
    of the architecture. A developer can write a custom gesture tracker and
    register it in the architecture. The tracker can use any type of detection
    logic internally, as long as it translates low-level events to high-level
    gestures.

    An example of a possible gesture tracker implementation is a
    ``transformation tracker'' that detects rotation, scaling and translation
    gestures.

    \section{Serving multiple applications}
    \label{sec:daemon}

    The design of the architecture is essentially complete with the components
    specified in this chapter. However, one specification has not yet been
    discussed: the ability to address the architecture using a method of
    communication independent of the application's programming language.

    If the architecture and a gesture-based application are written in the same
    language, the main loop of the architecture can run in a separate thread of
    the application. If the application is written in a different language, the
    architecture has to run in a separate process. Since the application needs
    to respond to gestures that are triggered by the architecture, there must
    be a communication layer between the separate processes.

    A common and efficient way of communication between two separate processes
    is through the use of a network protocol. In this particular case, the
    architecture can run as a daemon\footnote{``daemon'' is a name Unix uses to
    indicate that a process runs as a background process.} process, listening
    to driver messages and triggering gestures in registered applications.

    \vspace{-0.3em}
    \daemondiagram

    An advantage of a daemon setup is that it can serve multiple applications
    at the same time. Alternatively, each application that uses gesture
    interaction would start its own instance of the architecture in a separate
    process, which would be less efficient. The network communication layer
    also allows the architecture and a client application to run on separate
    machines, thus distributing computational load. The other machine may even
    use a different operating system.

%\section{Example usage}
%\label{sec:example}
%
%This section describes an extended example to illustrate the data flow of
%the architecture. The example application listens to tap events on a button
%within an application window. The window also contains a draggable circle.
%The application window can be resized using \emph{pinch} gestures. Figure
%\ref{fig:examplediagram} shows the architecture created by the pseudo code
%below.
%
%\begin{verbatim}
%initialize GUI framework, creating a window and nessecary GUI widgets
%
%create a root event area that synchronizes position and size with the application window
%define 'rotation' gesture handler and bind it to the root event area
%
%create an event area with the position and radius of the circle
%define 'drag' gesture handler and bind it to the circle event area
%
%create an event area with the position and size of the button
%define 'tap' gesture handler and bind it to the button event area
%
%create a new event server and assign the created root event area to it
%
%start the event server in a new thread
%start the GUI main loop in the current thread
%\end{verbatim}
%
%\examplediagram

\chapter{Implementation and test applications}
\label{chapter:testapps}

A reference implementation of the design has been written in Python. Two test
applications have been created to test if the design ``works'' in a practical
application, and to detect its flaws. One application is mainly used to test
the gesture tracker implementations. The other application uses multiple event
areas in a tree structure, demonstrating event delegation and propagation. The
second application also defines a custom gesture tracker.

To test multi-touch interaction properly, a multi-touch device is required. The
University of Amsterdam (UvA) has provided access to a multi-touch table from
PQlabs. The table uses the TUIO protocol \cite{TUIO} to communicate touch
events. See appendix \ref{app:tuio} for details regarding the TUIO protocol.

%The reference implementation and its test applications are a Proof of Concept,
%meant to show that the architecture design is effective.
%that translates TUIO messages to some common multi-touch gestures.

\section{Reference implementation}
\label{sec:implementation}

The reference implementation is written in Python and available at
\cite{gitrepos}. The following component implementations are included:

\textbf{Event drivers}
\begin{itemize}
    \item TUIO driver, using only the support for simple touch points with an
        $(x, y)$ position.
\end{itemize}

\textbf{Event areas}
\begin{itemize}
    \item Circular area
    \item Rectangular area
    \item Polygon area
    \item Full screen area
\end{itemize}

\textbf{Gesture trackers}
\begin{itemize}
    \item Basic tracker, supports $point\_down,~point\_move,~point\_up$ gestures.
    \item Tap tracker, supports $tap,~single\_tap,~double\_tap$ gestures.
    \item Transformation tracker, supports $rotate,~pinch,~drag,~flick$ gestures.
\end{itemize}

The implementation does not include a network protocol to support the daemon
setup as described in section \ref{sec:daemon}. Therefore, it is only usable in
Python programs. The two test programs are also written in Python.

The event area implementations contain some geometric functions to determine
whether an event should be delegated to an event area. All gesture trackers
have been implemented using an imperative programming style. Technical details
about the implementation of gesture detection are described in appendix
\ref{app:implementation-details}.

\section{Full screen Pygame application}

%The goal of this application was to experiment with the TUIO
%protocol, and to discover requirements for the architecture that was to be
%designed. When the architecture design was completed, the application was rewritten
%using the new architecture components. The original variant is still available
%in the ``experimental'' folder of the Git repository \cite{gitrepos}.

An implementation of the detection of some simple multi-touch gestures (single
tap, double tap, rotation, pinch and drag) using Processing\footnote{Processing
is a Java-based programming environment with an export possibility for Android.
See also \cite{processing}.} can be found in a forum on the Processing website
\cite{processingMT}. The application has been ported to Python and adapted to
receive input from the TUIO protocol. The implementation is fairly simple, but
it yields some appealing results (see figure \ref{fig:draw}). In the original
application, the detection logic of all gestures is combined in a single class
file. As predicted by the GART article \cite{GART}, this leads to over-complex
code that is difficult to read and debug.

The original application code consists of two main classes. The ``multi-touch
server'' starts a ``TUIO server'' that translates TUIO events to ``point
\{down,move,up\}'' events. Detection of ``tap'' and ``double tap'' gestures is
performed immediately after an event is received. Other gesture detection runs
in a separate thread, using the following loop:

\begin{verbatim}
60 times per second do:
    detect `single tap' based on the time since the latest `tap' gesture

    if points have been moved, added or removed since last iteration do:
        calculate the centroid of all points
        detect `drag' using centroid movement
        detect `rotation' using average orientation of all points to centroid
        detect `pinch' using average distance of all points to centroid
\end{verbatim}

There are two problems with the implementation described above. In the first
place, low-level events are not grouped before gesture detection. The gesture
detection uses all events for a single gesture. Therefore, only one element at
a time can be rotated/resized etc. (see also section \ref{sec:areas}).
Secondly, all detection code is located in the same class file. To extend the
application with new gestures, a programmer must extend the code in this class
file and therefore understand its structure. Since the main loop calls specific
gesture detection components explicitly in a certain order, the programmer must
alter the main loop to call custom gesture detection code. This is a problem
because this way of extending code is not scalable over time. The class file
would become more and more complex when extended with new gestures. The two
problems have been solved using event areas and gesture trackers from the
reference implementation. The gesture detection code has been separated into
two different gesture trackers, which are the ``tap'' and ``transformation''
trackers mentioned in section \ref{sec:implementation}.

The positions of all touch objects and their centroid are drawn using the
Pygame library. Since the Pygame library does not provide support to find the
location of the display window, the root event area captures events in the
entire screen surface. The application can be run either full screen or in
windowed mode. If windowed, screen-wide gesture coordinates are mapped to the
size of the Pyame window. In other words, the Pygame window always represents
the entire touch surface. The output of the application can be seen in figure
\ref{fig:draw}.

\begin{figure}[h!]
    \center
    \includegraphics[scale=0.4]{data/pygame_draw.png}
    \caption{Output of the experimental drawing program. It draws all touch
    points and their centroid on the screen (the centroid is used for rotation
    and pinch detection). It also draws a green rectangle which responds to
    rotation and pinch events.}
    \label{fig:draw}
\end{figure}

\section{GTK+/Cairo application}
\label{sec:testapp}

The second test application uses the GIMP toolkit (GTK+) \cite{GTK} to create
its user interface. Since GTK+ defines a main event loop that is started in
order to use the interface, the architecture implementation runs in a separate
thread.

The application creates a main window, whose size and position are synchronized
with the root event area of the architecture. The synchronization is handled
automatically by a \texttt{GtkEventWindow} object, which is a subclass of
\texttt{gtk.Window}. This object serves as a layer that connects the event area
functionality of the architecture to GTK+ windows. The following Python code
captures the essence of the synchronization layer:

\begin{verbatim}
class GtkEventWindow(Window):
    def __init__(self, width, height):
        Window.__init__(self)

        # Create an event area to represent the GTK window in the gesture
        # detection architecture
        self.area = RectangularArea(0, 0, width, height)

        # The "configure-event" signal is triggered by GTK when the position or
        # size of the window are updated
        self.connect("configure-event", self.sync_area)

    def sync_area(self, win, event):
        # Synchronize the position and size of the event area with that of the
        # GTK window
        self.area.width = event.width
        self.area.height = event.height
        self.area.set_position(*event.get_coords())
\end{verbatim}

The application window contains a number of polygons which can be dragged,
resized and rotated. Each polygon is represented by a separate event area to
allow simultaneous interaction with different polygons. The main window also
responds to transformation, by transforming all polygons. Additionally, double
tapping on a polygon changes its color.

An ``overlay'' event area is used to detect all fingers currently touching the
screen. The application defines a custom gesture tracker, called the ``hand
tracker'', which is used by the overlay. The hand tracker uses distances
between detected fingers to detect which fingers belong to the same hand. The
application draws a line from each finger to the hand it belongs to, as visible
in figure \ref{fig:testapp}.

Note that the overlay event area, though covering the entire screen surface, is
not used as the root of the event area tree. Instead, the overlay is placed on
top of the application window (being a rightmost sibling of the application
window event area in the tree). This is necessary, because the transformation
trackers in the application window stop the propagation of events. The hand
tracker needs to capture all events to be able to give an accurate
representations of all fingers touching the screen Therefore, the overlay
should delegate events to the hand tracker before they are stopped by a
transformation tracker.  Placing the overlay over the application window forces
the screen event area to delegate events to the overlay event area first. The
event area implementation delegates events to its children in right-to left
order, because area's that are added to the tree later are assumed to be
positioned over their previously added siblings.

\begin{figure}[h!]
    \center
    \includegraphics[scale=0.35]{data/testapp.png}
    \caption{
        Screenshot of the second test application. Two polygons can be dragged,
        rotated and scaled. Separate groups of fingers are recognized as hands,
        each hand is drawn as a centroid with a line to each finger.
    }
    \label{fig:testapp}
\end{figure}

To manage the propagation of events used for transformations and tapping, the
applications arranges its event areas in a tree structure as described in
section \ref{sec:tree}. Each transformable event area has its own
``transformation tracker'', which stops the propagation of events used for
transformation gestures. Because the propagation of these events is stopped,
overlapping polygons do not cause a problem. Figure \ref{fig:testappdiagram}
shows the tree structure used by the application.

\testappdiagram

\section{Conclusions}

To support different devices, there must be an abstraction of device drivers so
that gesture detection can be performed on a common set of low-level events.
This abstraction is provided by the event driver.

% Door input van meerdere drivers door dezelfde event driver heen te laten gaan
% is er ondersteuning voor meerdere apparaten tegelijkertijd.

Gestures must be able to occur within a certain area of a touch surface that is
covered by an application widget. Therefore, low-level events must be divided
into separate groups before any gesture detection is performed. Event areas
provide a way to accomplish this. Overlapping event areas are ordered in a tree
structure that can be synchronized with the widget tree of the application.
Some applications require the ability to handle an event exclusively for an
event area. An event propagation mechanism provides a solution for this: the
propagation of an event in the tree structure can be stopped after gesture
detection in an event area.

The detection of complex gestures can be approached in several ways. If
explicit detection code for different gesture is not managed well, program code
can become needlessly complex. A tracker-based design, in which the detection
of different types of gesture is separated into different gesture trackers,
reduces complexity and provides a way to extend a set of detection algorithms.
A gesture trackers implementation is flexible, e.g. complex detection
algorithms such as machine learning can be used simultaneously with other
gesture trackers that use explicit detection.

\chapter{Suggestions for future work}
\label{chapter:futurework}

\section{A generic method for grouping events}
\label{sec:eventfilter}

As mentioned in section \ref{sec:areas}, the concept of an event area is based
on the assumption that the set of originating events that form a particular
gesture, can be determined based exclusively on the location of the events.
Since this thesis focuses on multi-touch surface based devices, and every
object on a multi-touch surface has a position, this assumption is valid.
However, the design of the architecture is meant to be more generic; to provide
a structured design for managing gesture detection.

An in-air gesture detection device, such as the Microsoft Kinect \cite{kinect},
provides 3D positions. Some multi-touch tables work with a camera that can also
determine the shape and rotational orientation of objects touching the surface.
For these devices, events delegated by the event driver have more parameters
than a 2D position alone. The term ``area'' is not suitable to describe a group
of events that consist of these parameters.

A more generic term for a component that groups similar events is the
\emph{event filter}. The concept of an event filter is based on the same
principle as event areas, which is the assumption that gestures are formed from
a subset of all events. However, an event filter takes all parameters of an
event into account. An application on the camera-based multi-touch table could
be to group all objects that are triangular into one filter, and all
rectangular objects into another. Or, to separate small finger tips from large
ones to be able to recognize whether a child or an adult touches the table.

\section{Using a state machine for gesture detection}

All gesture trackers in the reference implementation are based on the explicit
analysis of events. Gesture detection is a widely researched subject, and the
separation of detection logic into different trackers allows for multiple types
of gesture detection in the same architecture. An interesting question is
whether multi-touch gestures can be described in a formal way so that explicit
detection code can be avoided.

\cite{GART} and \cite{conf/gw/RigollKE97} propose the use of machine learning
to recognize gestures. To use machine learning, a set of input events forming a
particular gesture must be represented as a feature vector. A learning set
containing a set of feature vectors that represent some gesture ``teaches'' the
machine what the feature of the gesture looks like.

An advantage of using explicit gesture detection code is the fact that it
provides a flexible way to specify the characteristics of a gesture, whereas
the performance of feature vector-based machine learning is dependent on the
quality of the learning set.

A better method to describe a gesture might be to specify its features as a
``signature''. The parameters of such a signature must be be based on input
events. When a set of input events matches the signature of some gesture, the
gesture is be triggered. A gesture signature should be a complete description
of all requirements the set of events must meet to form the gesture.

A way to describe signatures on a multi-touch surface can be by the use of a
state machine of its touch objects. The states of a simple touch point could be
${down, move, hold, up}$ to indicate respectively that a point is put down, is
being moved, is held on a position for some time, and is released. In this
case, a ``drag'' gesture can be described by the sequence $down - move - up$
and a ``select'' gesture by the sequence $down - hold$. If the set of states is
not sufficient to describe a desired gesture, a developer can add additional
states. For example, to be able to make a distinction between an element being
``dragged'' or ``thrown'' in some direction on the screen, two additional
states can be added: ${start, stop}$ to indicate that a point starts and stops
moving. The resulting state transitions are sequences $down - start - move -
stop - up$ and $down - start - move - up$ (the latter does not include a $stop$
to indicate that the element must keep moving after the gesture had been
performed). The two sequences distinguish a ``drag'' gesture from a ``flick''
gesture respectively.

An additional way to describe even more complex gestures is to use other
gestures in a signature. An example is to combine $select - drag$ to specify
that an element must be selected before it can be dragged.

The application of a state machine to describe multi-touch gestures is a
subject well worth exploring in the future.

\section{Daemon implementation}

Section \ref{sec:daemon} proposes the use of a network protocol to communicate
between an architecture implementation and (multiple) gesture-based
applications, as illustrated in figure \ref{fig:daemon}. The reference
implementation does not support network communication. If the architecture
design is to become successful in the future, the implementation of network
communication is a must. ZeroMQ (or $\emptyset$MQ) \cite{ZeroMQ} is a
high-performance software library with support for a wide range of programming
languages. A good basis for a future implementation could use this library as
the basis for its communication layer.

If an implementation of the architecture will be released, a good idea would be
to do so within a community of application developers. A community can
contribute to a central database of gesture trackers, making the interaction
from their applications available for use in other applications.

Ideally, a user can install a daemon process containing the architecture so
that it is usable for any gesture-based application on the device. Applications
that use the architecture can specify it as being a software dependency, or
include it in a software distribution.

\bibliographystyle{plain}
\bibliography{report}{}

\appendix

\chapter{The TUIO protocol}
\label{app:tuio}

The TUIO protocol \cite{TUIO} defines a way to geometrically describe tangible
objects, such as fingers or objects on a multi-touch table. Object information
is sent to the TUIO UDP port (3333 by default).

For efficiency reasons, the TUIO protocol is encoded using the Open Sound
Control \cite[OSC]{OSC} format. An OSC server/client implementation is
available for Python: pyOSC \cite{pyOSC}.

A Python implementation of the TUIO protocol also exists: pyTUIO \cite{pyTUIO}.
However, the execution of an example script yields an error regarding Python's
built-in \texttt{socket} library. Therefore, the reference implementation uses
the pyOSC package to receive TUIO messages.

The two most important message types of the protocol are ALIVE and SET
messages. An ALIVE message contains the list of session id's that are currently
``active'', which in the case of multi-touch a table means that they are
touching the screen. A SET message provides geometric information of a session
id, such as position, velocity and acceleration.

Each session id represents an object. The only type of objects on the
multi-touch table are what the TUIO protocol calls ``2DCur'', which is a (x, y)
position on the screen.

ALIVE messages can be used to determine when an object touches and releases the
screen. For example, if a session id was in the previous message but not in the
current, The object it represents has been lifted from the screen.

SET provide information about movement. In the case of simple (x, y) positions,
only the movement vector of the position itself can be calculated. For more
complex objects such as fiducials, arguments like rotational position and
acceleration are also included.

ALIVE and SET messages can be combined to create ``point down'', ``point move''
and ``point up'' events.

TUIO coordinates range from $0.0$ to $1.0$, with $(0.0, 0.0)$ being the left
top corner of the screen and $(1.0, 1.0)$ the right bottom corner. To focus
events within a window, a translation to window coordinates is required in the
client application, as stated by the online specification
\cite{TUIO_specification}:
\begin{quote}
    In order to compute the X and Y coordinates for the 2D profiles a TUIO
    tracker implementation needs to divide these values by the actual sensor
    dimension, while a TUIO client implementation consequently can scale these
    values back to the actual screen dimension.
\end{quote}

\chapter{Gesture detection in the reference implementation}
\label{app:implementation-details}

The reference implementation contains three gesture tracker implementations,
which are described in sections \ref{sec:basictracker} to
\ref{sec:transformationtracker}. Section \ref{sec:handtracker} describes the
custom ``hand tracker'' that is used by the test application from section
\ref{sec:testapp}.

\section{Basic tracker}
\label{sec:basictracker}

The ``basic tracker'' implementation exists only to provide access to low-level
events in an application. Low-level events are only handled by gesture
trackers, not by the application itself. Therefore, the basic tracker maps
\emph{point\_\{down,move,up\}} events to equally named gestures that can be
handled by the application.

\section{Tap tracker}
\label{sec:taptracker}

The ``tap tracker'' detects three types of tap gestures:

\begin{enumerate}
    \item The basic \emph{tap} gesture is triggered when a touch point releases
        the touch surface within a certain time and distance of its initial
        position. When a \emph{point\_down} event is received, its location is
        saved along with the current timestamp. On the next \emph{point\_up}
        event of the touch point, the difference in time and position with its
        saved values are compared with predefined thresholds to determine
        whether a \emph{tap} gesture should be triggered.
    \item A \emph{double tap} gesture consists of two sequential \emph{tap}
        gestures that are located within a certain distance of each other, and
        occur within a certain time window. When a \emph{tap} gesture is
        triggered, the tracker saves it as the ``last tap'' along with the
        current timestamp. When another \emph{tap} gesture is triggered, its
        location and the current timestamp are compared with those of the
        ``last tap'' gesture to determine whether a \emph{double tap} gesture
        should be triggered. If so, the gesture is triggered at the location of
        the ``last tap'', because the second tap may be less accurate.
    \item A separate thread handles detection of \emph{single tap} gestures at
        a rate of thirty times per second. When the time since the ``last tap''
        exceeds the maximum time between two taps of a \emph{double tap}
        gesture, a \emph{single tap} gesture is triggered.
\end{enumerate}

The \emph{single tap} gesture exists to be able to make a distinction between
single and double tap gestures. This distinction is not possible with the
regular \emph{tap} gesture, since the first \emph{tap} gesture has already been
handled by the application when the second \emph{tap} of a \emph{double tap}
gesture is triggered.

\section{Transformation tracker}
\label{sec:transformationtracker}

The transformation tracker triggers \emph{rotate}, \emph{pinch}, \emph{drag}
and \emph{flick} gestures. These gestures use the centroid of all touch points.
A \emph{rotate} gesture uses the difference in angle relative to the centroid
of all touch points, and \emph{pinch} uses the difference in distance. Both
values are normalized using division by the number of touch points $N$. A
\emph{pinch} gesture contains a scale factor, and therefore uses a division of
the current by the previous average distance to the centroid. Any movement of
the centroid is used for \emph{drag} gestures. When a dragged touch point is
released, a \emph{flick} gesture is triggered in the direction of the
\emph{drag} gesture.

Figure \ref{fig:transformationtracker} shows an example situation in which a
touch point is moved, triggering a \emph{pinch} gesture, a \emph{rotate}
gesture and a \emph{drag} gesture.

\transformationtracker

The \emph{pinch} gesture in figure \ref{fig:pinchrotate} uses the ratio
$d_2:d_1$ to calculate its $scale$ parameter. Note that the difference in
distance $d_2 - d_1$ and the difference in angle $\alpha$ both relate to a
single touch point. The \emph{pinch} and \emph{rotate} gestures that are
triggered relate to all touch points, using the average of distances and
angles. Since all except one of the touch points have not moved, their
differences in distance and angle are zero. Thus, the averages can be
calculated by dividing the differences in distance and angle of the moved touch
point by the number of touch points $N$. The $scale$ parameter represents the
scale relative to the previous situation, which results in the following
formula:
$$pinch.scale = \frac{d_1 + \frac{d_2 - d_1}{N}}{d_1}$$
The angle used for the \emph{rotate} gesture is only divided by the number of
touch points to obtain an average rotation of all touch points:
$$rotate.angle = \frac{\alpha}{N}$$

\section{Hand tracker}
\label{sec:handtracker}

The hand tracker sees each touch point as a finger. Based on a predefined
distance threshold, each finger is assigned to a hand. Each hand consists of a
list of finger locations, and the centroid of those locations.

When a new finger is detected on the touch surface (a \emph{point\_down} event),
the distance from that finger to all hand centroids is calculated. The hand to
which the distance is the shortest can be the hand that the finger belongs to.
If the distance is larger than the predefined distance threshold, the finger is
assumed to be a new hand and \emph{hand\_down} gesture is triggered. Otherwise,
the finger is assigned to the closest hand. In both cases, a
\emph{finger\_down} gesture is triggered.

Each touch point is assigned an ID by the reference implementation. When the
hand tracker assigns a finger to a hand after a \emph{point\_down} event, its
touch point ID is saved in a hash map\footnote{In computer science, a hash
table or hash map is a data structure that uses a hash function to map
identifying values, known as keys (e.g., a person's name), to their associated
values (e.g., their telephone number). Source:
\url{http://en.wikipedia.org/wiki/Hashmap}} with the \texttt{Hand} object. When
a finger moves (a \emph{point\_move} event) or releases the touch surface
(\emph{point\_up}), The corresponding hand is loaded from the hash map and
triggers a \emph{finger\_move} or \emph{finger\_up} gesture. If a released
finger is the last of a hand, that hand is removed with a \emph{hand\_up}
gesture.

\end{document}
