Tilt-Based Automatic Zooming and Scaling in Mobile
`Devices – A State-Space Implementation
`
`Parisa Eslambolchilar1 and Roderick Murray-Smith1,2
`
`1 Hamilton Institute, National University of Ireland, NUI, Maynooth, Co.Kildare, Ireland
`parisa.eslambolchilar@may.ie
`
`2 Department of Computing Science, Glasgow University, Glasgow G12 8QQ, Scotland
`rod@dcs.gla.ac.uk
`
`Abstract. We provide a dynamic systems interpretation of the coupling of in-
`ternal states involved in speed-dependent automatic zooming, and test our im-
`plementation on a text browser on a Pocket PC instrumented with an acceler-
`ometer. The dynamic systems approach to the design of such continuous
`interaction interfaces allows the incorporation of analytical tools and construc-
`tive techniques from manual and automatic control theory. We illustrate ex-
`perimental results of the use of the proposed coupled navigation and zooming
`interface with classical scroll and zoom alternatives.
`
`1 Introduction
`
`Navigation techniques such as scrolling (or panning) and zooming are essential com-
`ponents of mobile device applications such as map browsing and reading text docu-
`ments, allowing the user access to a larger information space than can be viewed on
`the small screen. Scrolling allows the user to move to different locations, while zoom-
`ing allows the user to view a target at different scales. However, the restrictions in
`screen space on mobile devices make it difficult to browse a large document effi-
`ciently. Using the traditional scroll bar, the user must move back and forth between
`the document and the scroll bar, which can increase the effort required to use the in-
`terface. In addition, in a long document, a small movement of the handle can cause a
`sudden jump to a distant location, resulting in disorientation and frustration.
`Speed-dependent automatic zooming is a relatively new navigation technique [7, 8,
`14, 22, 25, 26] that unifies rate-based scrolling and zooming to overcome these limita-
`tions. The user controls the scrolling speed only, and the system automatically adjusts
`the zoom level so that the speed of visual flow across the screen remains constant. Us-
`ing this technique, the user can smoothly locate a distant target in a large document
`without having to manually interweave zooming and scrolling, and without becoming
`disoriented by extreme visual flow.
`In this paper we demonstrate that, as suggested by Igarashi and Hinckley [14],
`SDAZ is well suited to implementation on mobile devices instrumented with tilt sen-
`sors, which can then be comfortably controlled in a single-handed fashion. We also
`describe an alternative stylus controlled implementation for the PocketPC. A further
`contribution is the use of a state-space formulation of speed dependent zooming,
`
`S. Brewster and M. Dunlop (Eds.): MobileHCI 2004, LNCS 3160, pp. 120–131, 2004.
`© Springer-Verlag Berlin Heidelberg 2004
`
`SCEA Ex. 1047 Page 1
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`

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`Tilt-Based Automatic Zooming and Scaling in Mobile Devices 121
`
`which we believe is a promising reformulation of the technique, which opens the path
`to the use of analytic tools from optimal and manual control theory.
`
`2 Speed-Dependent Automatic Zooming – A Brief Review
`
`Several techniques have been proposed to improve the manipulation of scroll bars
`[14, 19]. They allow the user to control scrolling speed, enabling fine positioning in
`large documents. LensBar [18] combines these techniques with interactive filtering
`and semantic zooming, and also provides explicit control of zooming via horizontal
`motion of the mouse cursor. A rate-based scrolling interface is described in [29] that
`maps displacement of the input device to the velocity of scrolling.
`Zoomable user interfaces, such as Pad and Pad++ [4], use continuous zooming as a
`central navigation tool. The objects are spatially organized in an infinite two-
`dimensional information space, and the user accesses a target object using panning
`and zooming operations. A notable problem with the original zoomable interfaces is
`that they require explicit control of both panning and zooming, and it is sometimes
`difficult for the user to coordinate them. The user can get lost in the infinite informa-
`tion space [16]. Bimanual approaches also exist, such as that of Guiard et al. [11]
`where a joystick in one hand controlled zoom level, and a mouse in the other provided
`navigation. They showed that by using zooming interfaces, bit rates far beyond those
`possible in physical selection tasks become possible.
`Information visualization techniques, such as Fisheye Views [9, 12], Perspective
`Wall [17], and the Document Lens [21] also address the problem of information over-
`load by distorting the view of documents. The focused area is magnified, while the
`non-focused areas are squashed but remain in spatial context. The user specifies the
`next focal point by clicking or panning. Van Wijk derived an optimal trajectory for
`panning and zooming in [24], for known start and end points.
`The particular input device used can also influence the effectiveness of rate con-
`trol. An experiment on 6 DOF input control [29] showed that rate control is more ef-
`fective with isometric or elastic devices, because of their self-centring nature. It is
`also reported that an isometric rate-control joystick [2] can surpass a traditional scroll
`bar and a mouse with a finger wheel [29]. Another possibility is to change the rate of
`scrolling or panning in response to tilt, as demonstrated by Rekimoto [20] as well as
`Harrison et al. [13], suitable for small screen devices like mobiles phones and PDAs.
`A common problem with scrolling and zooming interfaces is that when users are
`zoomed out for orientation, there is not enough detail to do any ‘real work’. When
`they are zoomed in sufficiently to see detail, the context is lost. To reduce this prob-
`lem, multiple windows can be provided, each with pan and zoom capability. Although
`this is reasonable for small information spaces, the many windows required by large
`spaces often lead to usability problems due to excessive screen clutter and window
`overlap. An alternative strategy is to have one window containing a small overview,
`while a second window shows a large more detailed view [3, 10]. The small overview
`contains a rectangle that can be moved and resized, and its contents are shown at a
`larger scale in the large view. This strategy, however, requires extra space for the
`overview and forces the viewer to mentally integrate the detail and context views. An
`operational overhead is also required, because the user must regularly move the
`mouse between the detail and context windows.
`
`SCEA Ex. 1047 Page 2
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`

`
`122 P. Eslambolchilar and R. Murray-Smith
`
`Speed-dependent automatic zooming (SDAZ) is a navigation technique first pro-
`posed by Igarashi & Hinckley [14]. It couples rate-based scrolling with automatic
`zooming to overcome the limitations of typical scrolling interfaces and to prevent ex-
`treme visual flow. This means that as a user scrolls faster the system automatically
`zooms out, providing a constant information flow across the screen. This allows users
`to efficiently scroll a document without having to manually switch between zooming
`and scrolling or becoming disoriented by fast visual flow, and results in a smooth
`curve in the space-scale diagram. In traditional manual zooming interfaces, the user
`has to interleave zooming and scrolling (or panning); thus the resulting pan-zoom tra-
`jectory forms a zigzag line. Cockburn et al. [7, 8, 22, 25, 26] presented further devel-
`opments, with a usability study of performance-improved SDAZ prototypes.
`
`3 Dynamics and Interaction
`
`In this paper we use systems of differential equations to describe the interaction be-
`tween user and computer. Skeptics might question this “Why introduce dynamics,
`when dynamic systems tend to be more difficult to control than static ones? Vehicle
`control systems tend to go to great trouble to hide the underlying dynamics of the ve-
`hicle from the driver.”
`We explicitly include dynamics because we can only control what we can perceive,
`and while, in principle, we can navigate instantly in an arbitrary information space,
`given a static interaction mechanism (e.g. clicking on a scroll bar), if we are depend-
`ent on feedback to be displayed while pursuing our goals, there will be upper limits
`on the speed at which the display can change. This is especially true in cases where
`there is uncertainty in the user’s mind about where to go, and when they have the op-
`tion to change their goal on route, as more information becomes available. In order to
`cope with this, interface designers have a long history of hand-crafting transition ef-
`fects in a case-by-case manner. Nonlinear mouse transfer functions are long-
`established examples of finely-tuned dynamic systems driven by user input.
`One of our long-term goals is to investigate whether describing the dynamics of in-
`teraction using the tools of control engineers allows us a more consistent approach to
`analyzing, developing and comparing the ‘look-and-feel’ of an interface, or in control
`terms, the ‘handling qualities’. Control synthesis often focuses on analysis of cou-
`pling among system states. Speed-dependent zooming is an obvious example of this,
`but if we generalize the approach to other interaction scenarios, with possibly a larger
`number of interacting states/inputs, we will require more general methods to analyse
`the consequences of coupling effects. Control methods are likely to be especially im-
`portant for design for mobile devices, where sensor noise, disturbance rejection, sen-
`sor fusion, adaptive self-calibration and incorporating models of human control be-
`haviour are all important research challenges.
`In cases such as the use of accelerometers as input devices, the direct mapping of
`acceleration in the real world to acceleration in the interface provides an intuitive
`mapping, which also suggests a range of other affordances, especially for multi-modal
`feedback, which can then be utilized by interface designers. Real-world effects such
`as haptic feedback of springs, or friction linked to speed of motion are easy to repro-
`duce in a dynamic system, and we can choose to explicitly use these features to de-
`sign the system to encourage interaction to fall into a comfortable, natural rhythm.
`
`SCEA Ex. 1047 Page 3
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`Tilt-Based Automatic Zooming and Scaling in Mobile Devices 123
`
`Furthermore, the act of performing a continuous input trajectory to achieve a goal,
`creates proprioceptive feedback for the user which can then be associated with that
`particular task. The mechanisms of gesture recognition can be ‘opened up’ and explic-
`itly made visible during the motion, to provide a link for the user between the control
`input and the task completion. We describe a probabilistic, audio/vibrotactile ap-
`proach to this in [28], which can ease learning and reduce frustration.
`The use of dynamic models of interaction allows intelligent interaction, if the han-
`dling qualities of the dynamics of the interface are adapted depending on current in-
`ferred user goals. Using this approach, actions require less effort, the more likely the
`system’s interpretations of user intentions, equivalent to a fewer bits from the user, in
`communication terms. This was used by Barrett et al. in [2], and we used this ap-
`proach for text entry in Williamson & Murray-Smith [27], and the approach can be
`linked to methods which adapt the control-to-display ratio, such as Blanch et al. [5] in
`classical windows interfaces. These approaches, which work with relative input
`mechanisms, cannot be used if we use static mappings, such as a stylus touching an
`explicit point on the screen.
`
`4 Speed-Dependent Automatic Zooming on a Mobile Device
`
`Implementing the SDAZ technique on a mobile device with inertial sensing allows us
`to investigate a number of issues: the use of single-handed tilt-controlled navigation,
`which does not involve obscuring the small display; the usability consequences of tilt-
`ing the display; the relative strength of stylus-based speed-dependent zooming, com-
`pared to mouse and tilt-based control, and combinations of stylus, and tilt-based con-
`trol. If successful, the user should be able to target a position quickly without
`becoming annoyed or disoriented by extreme visual flow, and we want the technique
`to provide smooth transitions between the magnified local view and the global over-
`view, without the user having to manually change the document magnification factor.
`
`
`
`Fig. 1a
`Fig. 1. PocketPC and accelerometer attached to serial port (1a). Screen shots of the document
`browser (1b). The left picture shows a red box moving rapidly over the picture, the middle pic-
`ture shows the user has found the picture and landing there, and right picture shows the
`zoomed-in picture.
`
`Fig. 1b
`
`SCEA Ex. 1047 Page 4
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`

`
`124 P. Eslambolchilar and R. Murray-Smith
`
`4.1 Hardware/Software Environment
`
`We implemented this method using Embedded Visual C++ on an HP 5450 Pocket PC
`(Figure 1). Here, tilting the device moves the zooming-window. The accelerometer
`(Xsens P3C, 3 degree-of-freedom linear accelerometer) attached to the serial port of
`the Pocket PC provides the roll and pitch angles.
`
`4.2 Design and Implementation of Speed-Dependent Automatic Zooming
`
`State space modelling is a well-established way of presenting differential equations
`describing a dynamic system as a set of first-order differential equations. There is a
`wealth of knowledge and analysis techniques from systems theory, including design-
`ing estimators and controllers for multi–input–multi–output systems, optimal control,
`disturbance rejection, stability analysis and manual control theory [6]. State-space
`modelling allows us to model the internal dynamics of the system, as well as the
`overall input/output relationship as in transfer functions, so this method is an obvious
`candidate for the representation of the coupling between the user’s speed with zoom
`level. There are many advantages to modelling systems in state space, especially for
`multivariable problems, where the matrix formulation is particularly useful for analy-
`sis purposes.
`
`4.2.1 State Space Model
`For an introduction to the basic ideas, see any introductory control theory book, e.g.
`[1,6]. The generic form for the state equations is given by equation (1)
`=
`+
`
` )(xf
`X
`=
`
`
` )(xh
`Y
`where f(x), g(u) and h(x) can be nonlinear functions, and where X(t) is an n ×1 state
`vector where n is the number of states or system order, U(t) is a r ×1 input vector
`where r is the number of input functions, and Y(t) is a p ×1 output vector where p is
`the number of outputs. The more specific case of a linear system, (2)
`=
`+
`(cid:5)( )
`( )
`( )
`x t
`Ax t
`Bu t
`+
`=
`( )
`( )
`( )
`y t
`Cx t Du t
`
`rn × matrix
`nn × square matrix called the system matrix, B is an
`where A is an
`called the input matrix, C is a p n× matrix called the output matrix and D is a p r×
`matrix which represents any direct connection between the input and output.
`
` (1)
`
` (2)
`
`
`
`
`
`
`
` )(ug
`
`(cid:5)(cid:5)
`
`4.2.2 Coupling the User’s Velocity with the Zoom-Level
`In this section we show how an SDAZ-like approach couples the user’s motion with
`the zoom-level. The inputs to the system are the tilting angles measured using an ac-
`celerometer attached to the serial port of PDA, and in a second experiment the stylus
`)(1 tx
`position on the PDA touch screen. The state variables chosen are
`for position,
`)(2 tx
`)(3 tx
`for zoom, and the state equations are:
`
` for speed of scroll and
`
`SCEA Ex. 1047 Page 5
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`

`
`Tilt-Based Automatic Zooming and Scaling in Mobile Devices 125
`
`
` )(tx
`2
`
` )(tx
`3
`
`=
`=
`
`V
`Z
`
`∆=
`x
`1
`=
`
` ( , ),uxxf
`
`
`2
`1
`
` (3)
`
` (4)
`
`
`So the zoom-level is a function of position, velocity and tilting angle. An initial sug-
`gestion is to reproduce the standard second-order dynamics of a mass-spring-damper
`system, in the hope that giving the scrolling movement and zoom level some inertia
`will provide a physically intuitive interface. The first time-derivative of the state
`equations can be written as below, as a linearization of the system at a given velocity
`and zoom:
`
`
`
` (5)
`
` (6)
`
`
` (7)
`
`
`
` (8)
`
`=
`=
`)(
`)(
`txVtx
`1
`2
`−=
`R
`(cid:5)
`=
`)(
`Vtx
`2
`M
`−=
`b
`M
`
`(cid:5)(cid:5)
`
`=
`(cid:5)
`)(
`tx
`3
`
`(cid:5)
`Z
`
`)(
`tx
`2
`
`)(
`tu
`
`Mk
`
`
`
` )(tu
`
`Ma
`
`+
`)(
`tx
`3
`
`+
`)(
`tx
`2
`−+
`R
`M
`
`The standard matrix format of these equations is:
`
`
`This shows how a single-degree of freedom input can control both velocity and zoom-
`level. The non-zero off-diagonal elements of the A matrix indicate coupling among
`states, and the B matrix indicates how the inputs affect each state. This example could
`be represented as having zoom as an output equation, rather than state, and the cou-
`pling between zoom and speed comes only through the B matrix, which is not particu-
`larly satisfying. However, this paper is intended as an initial exploration of the area,
`and as more interesting behaviour can be obtained by fully interacting nonlinear equa-
`tions, such as those elegantly derived by van Wijk in [24], we have left it in this for-
`mat. In the experiments, R=1, M=1, k=1 and b=0, but we also experimented with
`varying the parameters, essentially including nonlinearities by a function relating ve-
`locity with zoom factor, as will be discussed in the next section. We include satura-
`tion terms for maximum and minimum zoom levels, and there can be specific rules
`for behaviour at the limits associated with the start and end of the document. For
`nonlinear functions we can locally linearise around any given state [x v z] leading to
`time-varying matrices A(t),B(t). We can analytically investigate the local dynamics
`for different operating points by, for example, looking at the eigenvalues of the A & B
`matrices to check for oscillatory (eigenvalues are complex conjugate pairs) or unsta-
`ble behaviour (real part of eigenvalue in right half plane – i.e. positive). For more
`background see any control textbook (e.g. [1, 6]). Importantly, the system itself might
`be stable, but when coupled with the time delay and lead-lag-dynamics of typical hu-
`man control behaviour, the combined closed loop system might be unstable, as in pi-
`lot-induced oscillations in aircraft control [15,23].
`
`SCEA Ex. 1047 Page 6
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`

`
`126 P. Eslambolchilar and R. Murray-Smith
`
`The dynamic systems implementation allows us to deviate from a static link be-
`tween speed and zoom level. In this paper, our basic assumption is that zoom should
`lead speed when speed increases, in order to avoid extreme visual flow. Zoom should,
`however, lag speed when |v| decreases, to allow the user to slow down but still main-
`tain the overview. This also allows, for example, the user to zoom out, without chang-
`ing position in the document, by repeated positive and negative acceleration.
`In order to move more rapidly through the document at high levels of zoom, in this
`paper, we adapted B by making ‘a’ in eqn. (8) a function of velocity. When speed is
`above the dead-zone threshold (here set to 0.1), a = 3 but below this threshold a=0.
`We wish to avoid rapid drop effects when user changes direction. To achieve this, we
`set a=a*0.2, when the sign of velocity and input differ. For practical implementation
`on a PDA we converted the continuous-time system to a discrete-time one [1], with
`the evaluation of a matrix exponential,
`sampling
`time h, which
`involves
`h
` .
`=Φ
`∫=Γ
`
`
`Ah
`
`e
`
` ,
`
`As
`
`e
`
`dsB
`
`0
`
`
`(khx
`
`(khy
`
`Φ=
`Γ+
`+
`)
`
`(khu
`)
`
`(khx
`h
`+
`=
`
`(khCx
`)
`
`(khDu
`)
`)
`
`)
`
`
`
`(9)
`
`
`
`A phase plane figure shows an example of a trajectory through this state-space for
`the SDAZ implementation on the Pocket PC (Figure 2). This gives some insight into
`the transient dynamics of large and small translations of position through the docu-
`ment.
`
`
`
`Fig. 2. Phase plane trajectories showing velocity against zoom (left), zoom-level against posi-
`tion (centre) and velocity against position (right), from a record of participant browsing a long
`document on the PocketPC.
`
`4.2.3 Control Mode
`We can now introduce transitions among control modes which alter the dynamics and
`the way user inputs are interpreted. A simple example of this approach uses state
`feedback to augment control behaviour, by making the state move towards some ref-
`(
`),x
`=
`−
` such that the new state equa-
`erence value r, we can create a control law
`rLu
`tions are
`
`=(cid:5)
`x
`=
`
`+
`−
`
`=
`Bu
`)
`xBL
`
`−
`BLx
`Ax
`+
`BLr
`
`Ax
`(
`A
`
`+
`
`BLr
`
`
`
`(10)
`
`
`
`SCEA Ex. 1047 Page 7
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`
`Tilt-Based Automatic Zooming and Scaling in Mobile Devices 127
`
`such that the system dynamics have changed from A to (A-BL). In the SDAZ imple-
`mentation in this paper, we switched from tilt-angle as acceleration, to tilt angle to in-
`dicate desired velocity, as soon as the speed passed the threshold at which zooming
`started. This made it easier for users to find and maintain a comfortable zoom level.
`Other similar examples can be created, where the interpretation of sensor inputs and
`their significance for control can adapt to context. Including position control, for ex-
`ample, would allow the user to tap on the screen to specify a goal, which is then dy-
`namically acquired. While on route to that goal, the user changes their mind, they can
`break out and switch again to velocity control.
`
`4.2.4 Calibrating SDAZ and the State Space Approach
`SDAZ has many parameters that can be tuned, usually treated as a series of interact-
`ing, but essentially separate equations. The state-space formulation allows multiple
`variables, and derivative effects (e.g. position, velocity, acceleration) can be coupled
`with zoom level, without any further coding, by just changing the entries of the A ma-
`trix, simulating combinations of springs, masses and damping effects.
`In SDAZ, the function linking zoom with velocity, z = f(v), can be nonlinear,
`including threshold effects. Examples include linear, with thresholds, exponential, and
`‘modified exponential’ [14,25]. Furthermore the document velocity v=g(δ) as a func-
`tion of control input (mouse displacement, tilt-angle, or stylus displacement, depend-
`ing on platform) tend to be static, linear, or piecewise linear functions [14, 25]. In the
`state-space representation, we need to reformulate these equations in terms of the
`time-derivatives of zoom and velocity, via the A and B matrices. For example, for
`ramp increases in speed, the modified exponential zoom-speed mapping corresponds
`to our suggestion of zoom leading speed, with the exponent being related to the dif-
`ference between the time constants for zoom and speed.
`To enhance the smoothness of the transition between the global overview and the
`magnified local view after a mouse button is pressed, Cockburn and Savage use a ‘fal-
`ling’ speed, and Igarashi & Hinckley [14] place a limit on the maximum time-
`derivative of zoom, with similar effect. The falling rate was calculated using trial and
`error – if the rate was too fast, the user felt motion sickness and lost their place in the
`document, whereas it being too small led to a sluggish interface. This can be repre-
`sented as a straightforward switch to a particular parameterization of the A matrix,
`which can be tuned to give an appropriate exponential decay in velocity or zoom.
`Related problems include rapid zooming in and out when making a rapid change of
`direction [14]. In the state-space representation, dealing with these issues becomes a
`matter of tuning the dynamics of the system by changing the A matrix, to make, for
`example, the time-constants associated with the zoom level larger than that of the
`speed, for regimes where speed is dropping.
`Gutwin [12], Igarashi & Hinckley [14] and Wallace [25] report the hunting effect
`problem when users overshoot the target due to the system zooming in as the user
`slows, the user then rapidly adjusts behaviour to compensate, which causes the system
`to zoom out again. One approach to this would be to switch to a ‘diving’ control mode
`if dz/dt < zthresh, where a=0, preventing zooming increases, unless a major change in
`velocity, occurs, which would switch the control mode back to velocity control.
`
`SCEA Ex. 1047 Page 8
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`

`
`128 P. Eslambolchilar and R. Murray-Smith
`
`5 Example Application – Document Browser for a PDA
`
`The document viewer was designed to use automatic zooming to browse PDF, PS and
`DOC files which had been converted to a image (PNG) file. BMP or PNG (Portable
`Network Graphics) files are more efficient, and have low rendering time. This in-
`creases the speed and smoothness of the browser, the implementation of which was
`simple but very efficient and smooth (although text tended to flicker during zooming
`because it was treated as a flat image). Equations (15) to (18) (previous section) show
`the formula used to calculate the relationship between the user’s hand motion (tilting
`PDA) and the zoom level from the document.
`For comparison we show trajectories of users using traditional scroll bars on the
`Pocket PC and a touch-screen based SDAZ implementation (Figure 3) for browsing a
`long document on PDA (Figure. 1b). The touch-screen based SDAZ and tilt-
`controlled SDAZ both use the same state-space model. The results in Figure 3 high-
`light the different navigation styles of the different interfaces, with the scroll bar ap-
`proach using a number of rapid translations through the document to find a paragraph
`in bottom of the document, and no use of zooming for an overview, while the two
`SDAZ implementations had smoother navigation, which also included smooth
`changes in zoom level.
`
`User Input
`
`Y screen position
`
`Y scroll position
`
`Y screen position
`
`User Input
`
`Time
`
`Time
`
`Time
`
`
`
`Fig. 3. Left picture shows the trajectory of one participant in using traditional scroll bars in
`browsing the long document, so y displacement is as long as the document. Middle picture
`shows the trajectory of the same participant in touch screen based SDAZ in browsing the long.
`
`
`
`Users found the touch screen-based mechanism intuitive and easy to use for brows-
`ing. Figure 4 presents the system’s inputs in three SDAZ applications to find the same
`paragraph used in scroll bar browser for tilt-based and touch screen controlled SDAZ.
`Also this figure presents an example run with tilt-based SDAZ, with augmented ve-
`locity control, as described in section 4.2.3, to browse the document to find 7 main
`headings. For comparison, the central plots in Figure 4 show tilt-based SDAZ without
`augmented velocity control on the same task, where fluctuations indicate that control-
`ling the zoom level was difficult, and hunting behaviour appears when users tried to
`land on the targets (e.g. t=20,40, 85, in middle figures).
`
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`Tilt-Based Automatic Zooming and Scaling in Mobile Devices 129
`
`6 User Feedback
`
`We asked five users from our research lab to work with the document browser using
`tilt-based SDAZ and touch screen-controlled SDAZ with and without augmented ve-
`locity control. Users who did the experiment without augmented velocity control sug-
`gested that adding a control option or a switch to control the zoom-level with velocity
`and tilting angles will make the system more comfortable to use. Most of them pro-
`posed if they could control level of zoom by tapping on the screen or pressing a key
`on PDA, the application would be easier to use.
`
`
`
`Fig. 4. Left picture tilt-based SDAZ with augmented velocity control, middle picture tilt-based
`SDAZ without augmented control and right picture touch-screen controlled SDAZ.
`
`In contrast, users who did their experiments with augmented velocity control were
`satisfied with the application in both tilt-based and touch screen-controlled modes.
`Some users complained that with tilt input, they had to tilt the device to angles which
`caused irritating reflections from the PocketPC screen. Users in both groups, with and
`without augmented control, commented that if they were involved with other tasks,
`(like answering the phone, working with PC, etc.) they would prefer the touch screen-
`controlled SDAZ because they imagined it would be difficult to stay in the desired
`position in the document, with a tilt-based SDAZ. Although this was beyond the
`scope of our initial experiments, a key factor in the usefulness of tilt-based SDAZ will
`be the ease with which the user can toggle tilt-control on and off, during tasks.
`
`SCEA Ex. 1047 Page 10
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`

`
`130 P. Eslambolchilar and R. Murray-Smith
`
`7 Conclusions
`
`We have presented a state-space, dynamic systems representation of the dynamic
`coupling involved in speed-dependent automatic zooming. We demonstrated the ap-
`plicability of the approach by implementing a speed-dependent zooming interface for
`a text browsing system on a PDA instrumented with an accelerometer, and with stylus
`control. We illustrated the behaviour of the different interfaces by plotting their trajec-
`tories in phase space and as time-series.
`Initial informal user evaluation of the implementation of SDAZ on a Pocket PC is
`positive, and users felt that this provided an intuitive solution to the problem of large
`documents and small displays. The tilt-controlled version can be used in a single-
`handed manner, without obscuring the screen, but because in the implementation
`tested, there was no toggle for tilt-control, users felt more comfortable with the stylus-
`controlled version.
`This approach has the potential to provide a very general framework for develop-
`ment, analysis and optimisation of interfaces which induce complex, but convenient
`coupling among multiple states, in order to cope with few degrees of freedom in in-
`put. It opens up the dynamics of the ‘look and feel’ of mobile applications based on
`continuous control metaphors, to analysis and design techniques from automatic and
`manual control theory [15, 23].
`
`Acknowledgements. The authors gratefully acknowledge the support of SFI BRG
`project Continuous Gestural Interaction with Mobile devices, Science Foundation
`Ireland grant 00/PI.1/C067, the MAC network - EC TMR grant HPRN-CT-1999-
`00107, and EPSRC grant GR/R98105/01.
`
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