Precise Selection Techniques for Multi-Touch Screens
Hrvoje Benko
Department of Computer Science
Columbia University
New York, NY
benko@cs.columbia.edu

Andrew D. Wilson, Patrick BaudischMicrosoft Research
One Microsoft Way
Redmond, WA{awilson, baudisch}@microsoft.com

ABSTRACTThe size of human fingers and the lack of sensing precision
can make precise touch screen interactions difficult. We
present a set of five techniques, called Dual Finger
Selections, which leverage the recent development of multitouch sensitive displays to help users select very small
targets. These techniques facilitate pixel-accurate targetingby adjusting the control-display ratio with a secondary
finger while the primary finger controls the movement of
the cursor. We also contribute a “clicking” technique, called
SimPress, which reduces motion errors during clicking andallows us to simulate a hover state on devices unable tosense proximity. We implemented our techniques on amulti-touch tabletop prototype that offers computer visionbased tracking. In our formal user study, we tested theperformance of our three most promising techniques(Stretch, X-Menu, and Slider) against our baseline (Offset),on four target sizes and three input noise levels. All threechosen techniques outperformed the control technique interms of error rate reduction and were preferred by ourparticipants, with Stretch being the overall performance andpreference winner.
Author Keywords

Touch screens, tabletop displays, two-finger, bi-manual,interaction techniques, precise target acquisition.
ACM Classification Keywords

H.5.2 [Information Interfaces and Presentation]: User
Interfaces — Input devices and strategies, Interaction
styles, Evaluation/methodology.
INTRODUCTION

The ability to directly touch and manipulate data on thescreen without using any intermediary devices has a verystrong appeal to users. In particular, novices benefit mostfrom the directness of touch screen displays. A fast learning
curve and inherent robustness (no movable parts) makes
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise,or republish, to post on servers or to redistribute to lists, requires priorspecific permission and/or a fee.
CHI 2006, April 22–27, 2006, Montréal, Québec, Canada.
Copyright 2006 ACM 1-59593-178-3/06/0004...$5.00.

Figure 1. Precise dual finger selection techniques
enable pixel-precise selections and manipulations
on multi-touch screens. This image shows the use of
Dual Finger X-Menu in selecting “slow 10X” mode.

touch screens an ideal interface for interacting with public
installations, such as information kiosks, automated teller
machines, ticketing machines, or gambling devices.
While touch screen use is widespread in special purpose
applications, the slow adoption of touch screens into moregeneral computing devices has be attributed to known
issues of relatively high error rates, arm fatigue, and lack ofprecision [2]. Due to technical restrictions, mostcommercially available touch screen devices in use today
are only capable of tracking a single point on the surface ofthe device. However, with the recent emergence of many
multi-touch prototype devices [1, 11, 14, 22, 25, 26],
research on multi-finger and multi-hand touch interactions
has increased [4, 7, 27]. In addition to dealing with thesame issues as the single-touch machines, the underlying
technology of multi-touch sensitive devices (such as visionbased sensing [25, 26]) often tends to make their input more
noisy.
When running software developed for a normal mouseinterface on such a touch screen, these issues become
problematic. Today’s WIMP (windows, icons, menus andpointing) user interfaces require frequent selection of verysmall targets. For example, window resize handles are often
just 4 pixels wide. Noisy input, lower tracking resolution,and large potential touch area of a finger now become aproblem. Furthermore, fingertips can occlude small targets
depriving users of visual feedback during target acquisition.

Also, the user’s hands and arms may contribute to the
occlusion problem. Depending on screen orientation, theuser may be forced to either look “under hand” (withhorizontally positioned screens) or “over hand” (withangled or vertically positioned screens). Finally, it is often
difficult to decide the optimal point in the finger’s contact
area which should anchor the cursor, leaving the usual
choice to the center of mass. This can lead to a small but
pervasive disconnect between the user’s expectations
regarding cursor position and what is actually being sensed
and computed.
These issues have been recognized by researchers who haveproposed several solutions: adding a fixed cursor offset[21], enlarging the target area [19], and providing on-screen
widgets to aid in selection [2]. Unlike previous work, we
explore the benefits of multi-touch capable devices toprovide fluid dual-finger interactions for pixel-accurate
targeting. In the techniques presented in this paper, thesecondary (non-pointing) finger can quickly modify orswitch cursor manipulation modes without disrupting the
primary (pointing) finger.
We present an iteratively designed set of five techniquesthat allow the user to simultaneously perform both cursor
steering and selection of assistance mode (in the form of
cursor offset, scale, speed reduction, or a combination). In
addition to the precise selection techniques, we contribute a“clicking” technique, called SimPress, which reduces
motion errors during clicking and allows us to simulate ahover state on devices unable to sense proximity.
RELATED WORK

Difficulties with precise interactions on touch screen
devices have been addressed before, initially by Potter et al.
[21]. Their Take-Off technique provides a cursor with afixed offset above the tip of a finger when the user istouching the screen. Lifting the finger off the screen
triggered selection (“click”). While this method is effective
for most targets sizes, it has been found ineffective when
the target size is smaller than 4 pixels. Sears andShneiderman[24]
explored
cursorstabilization
improvements that effectively slowed down the cursor
movement in various regions around the initial finger
contact point, thus allowing for pixel-precise selection.
While this method performed well for the target acquisitiontask, a precise steering task, such as drawing, would be harddue to varying cursor speed.
More recently, Albinsson and Zhai [2] explored several onscreen widgets for increasing precision while selecting
small targets on a touch screen. Their interactions were
designed to be used with touch screens capable of reporting
only a single contact point and therefore the users wererequired to execute multiple discrete steps before selecting
the target. These steps were delimited by the user lifting
their finger from the screen, thus impeding the overall
interaction performance. Interestingly, they observed thateven though their baseline zooming technique(ZoomPointing) performed best out of the four techniques
compared, its main drawback of losing overview or context
can be a significant problem in many applications.
Increasing the relative size of screen targets has also been
explored by scaling the display space [19] or scaling the
motor space [3, 6]. The work of Olwal and Feiner [19]experimented with hand gestures that activated various
levels of fish-eye distortion in the interface to facilitatetarget selection. Techniques that adaptively increase themotor space while leaving the displayed image unchanged,
such as those by Blanch et al. [6] or Baudisch et al. [3],show promising results without introducing screen
distortions, but require that the system know all target
locations. This information might not be available in manyof today’s applications. More importantly, such techniques
require the use of a relative pointing device such as amouse. Without such devices, they introduce anunpredictable cursor offset when applied directly to an
absolute pointing device such as a touch screen.
Buxton [8] identified that most current user interfaces
require an interaction model consisting of at least 3
different states (out-of-range, tracking, and dragging).
However, many touch sensitive devices can only reliably
sense location in one state thus making it hard to
disambiguate between dragging and tracking (hover).
The use of a stylus (pen) is generally preferred in many
interfaces that require precise interactions. However, whilea stylus has a much smaller tip, the associated issues withhand tremor and resolution make the selection task of small
targets more difficult than with a mouse. Ren and Moriya[23] report that a limiting size for stylus targets is about 1.8mm, below which even the stylus interaction requires
additional assistance.
Much research has been performed on bimanual interactionin user interfaces. In their pioneering work, Buxton andMyers [9] demonstrated that users tend to parallelizeinteraction tasks between hands thus gaining significant
performance improvements. Bier et al. [5], in their
Toolglass and Magic Lenses system, allowed the user tocontrol the transparent tool palette with the non-dominanthand, while the dominant hand controlled the primary
cursor with the mouse. This simultaneous bimanual
operation eliminated many inefficiencies typical of modal
interfaces. Some research by Kabbash et al. [16] points inthe opposite direction, claiming that requiring the user tocoordinate actions of their hands in order to perform twohanded interactions may complicate the overall task andslow performance.
Two-finger and two-handed interactions for the activationof various tools, menus and widgets have been explored byresearchers in many related fields: on tabletop surfaces [7,12, 22, 27], 3D volumetric displays [13], tangible userinterfaces [15, 20], virtual reality [10], and augmented
reality [4]. Rekimoto presented a novel capacitance-based
sensing architecture, called SmartSkin [22], together with

several multi-handed and multi-touch techniques forenabling the manipulation of objects projected on thesurface. Cutler et al. [10] present bimanual interaction
research where the user can perform two-handed
interactions to manipulate both their perspective and threedimensional models on a 3D tabletop display. Recently,
Malik et al. [18] explored using vision-based hand tracking
over a tabletop surface to perform multi-finger and whole-handgestures to interact with a remote display.
DESIGN GUIDELINES

To address the precision problem of touch screeninteractions we developed Dual Finger Selections, twofinger (or bi-manual) interactions that allow the user toimprove targeting precision when necessary withouthindering simple targeting tasks. Dual Finger Selections
were designed in an iterative fashion. During theirdevelopment we followed the following guidelines:
1) Keep simple things simple: The ability to directly touch
an object in order to select it (without any offset or
displacement) is probably the most appealing aspect oftouch screens. We aim to support this direct manner ofinteraction and require that further assistance isinvoked only when the user explicitly requests it.
2) Provide an offset to the cursor when so desired: In
addition to not occluding the cursor with the finger
while pointing, a spatial offset between the finger andthe cursor makes it possible to obtain a more
comfortable position when pointing to hard to reachareas (for example: the corner of the screen). The offset
should be user-invoked and temporary. Fixedpermanent offsets (such as the one used in the Take-Offtechnique [21]) require the users to continuouslycompensate their targeting even in situations when thetarget is large enough to be easily selected by direct
touch.
3) Enable the user to modify the control-display ratio:
Provide an increased control-display ratio when sodesired to aid in targeting and to reduce tracking noise.
This change of the control-display ratio should not
involve the pointing finger.
Before discussing the details of our dual finger selection
techniques, it is important to outline the device
requirements that enable our interactions.

finger, while the second contact is a secondary finger. The
primary finger is the finger that the user normally points
with and tends to be the index finger on the dominant hand.
The secondary finger is a helper finger which in Dual
Finger Selections can be any other finger on the same or
opposite hand. In most cases we observed that users used
the index finger on their non-dominant hand as thesecondary finger. With some interactions, a single-handed
operation is desired, and then the thumb of the dominant
hand can serve as a secondary finger.
SimPress Clicking

In addition to disambiguating between fingers, ourinteractions require that the device implement a clicking
operation distinct from merely coming in contact with thesurface. Previous techniques that address this issue, such as
Land-On or Take-Off [21], implicitly perform a “click”
when the contact between their finger and the surface iseither established or broken, respectively. Such techniques
provide a mechanism for clicking, but do not address theneeds of current user interfaces that require at least 3
different interaction states [8]: out-of-range, tracking (alsoknown as hover or proximity), and dragging. Both tracking
and dragging states require the contact position to becontinuously reported; however, most current touchsensitive devices only sense location when the contact is
actually touching the surface, making it difficult toapproximate those two states. A possible solution is to usepressure-sensing technology and map the increased pressure
to a dragging state, and light pressure to a tracking state.
Since our device does not report pressure directly, we
simulated a pressure-sensitive device by mapping thechanges in the finger’s contact area to the changes inpressure. In addition to applying different finger areas todifferent pressure states (which has been implemented in
the Synaptics touchpad devices as described by MacKenzie
et al. [17]), we have attempted to reduce cursor noise while
the user is changing the pressure states (clicking). The
stabilization of the cursor movement during clicking is a
crucial aspect of our technique, which we call SimPress(Simulated Pressure). SimPress requires the user to apply asmall rocking motion with their finger in order to perform a

ENABLING TECHNOLOGIESOur techniques require a multi-touch screen that is capableof simultaneously tracking at least two independentcontacts on the surface. We also assume that in addition to
the location of contacts, their contact areas are reported aswell. A brief description of our prototype multi-touch
device can be found later in this paper.
Since most touch screens and tabletops cannot identify
which of the individual user’s fingers or hands is touching
the surface without special gloves [7], we have assumed
that the first contact with a tabletop surface is a primarya)b)Figure 2. A small rocking motion of the user’s finger
triggers the SimPress clicking technique: a) tracking(hover) state, b) dragging (click) state. (The top left
corners show the actual area of contact detected byour device as well as the stabilized cursor location.)“click”, as seen in Figure 2. Since the user starts pointing
with their finger tip and then rocks the finger to click, theincrease in area happens predominately in one direction:from the tip point towards the user’s wrist. We used thisfact to stabilize the cursor position by fixing the cursor
location to the top middle point of the contact area, rather
then the center of mass (Figure 2). In our preliminary
experiments, we found that this point naturally moves muchless than the center point and therefore reduces the cursor
noise during clicking.
By fixing the cursor to the top-middle point, the user is alsoable to make a more drastic change in the contact area
without significantly disturbing the cursor location, which
aids in reduction of the unintentional clicks. Two thresholds
on contact area were established to disable spurious
switching between the clicking states due to noise or hand
tremor. Crossing the high threshold activates the click-on
state, and crossing the low threshold returns back to clickoff state. Due to the finger size differences, these high andlow thresholds should be automatically recalibrated foreach person. Currently, the calibration is done manually.
SimPress only works if the user is always approaching thetabletop from the same direction, otherwise the orientationof the hand and arm has to be taken into account. A future
improvement can potentially use the orientation of the click
itself to track the orientation of the user’s hand. However,given that in our experiments, the orientation of the userinterface was fixed, our users tended to orient themselves
straight-ahead. In our dual finger selection techniques, allclick events are always triggered by the primary finger.
DUAL FINGER SELECTIONS

Out of the five design prototypes, the first two present
simple two-finger extensions of the current state of the art.
However, those provide important starting points for ourlater designs and serve as baseline techniques forcomparisons. Therefore, we include them in our discussion.
Dual Finger Offset

Our initial and simplest Dual Finger Selection technique,called Dual Finger Offset, provides a user triggered cursor
offset. The cursor offset is not enabled by default. However,by placing a secondary finger anywhere on the surface, thecursor is subsequently offset with respect to the primary
finger by predefined fixed amount. This offset always
places the cursor above the primary finger. To
accommodate both left- and right-handed users the cursor isplaced to the left or to the right of the primary finger basedon the relative position of the secondary finger. For
example, by placing the secondary finger to the left of thesecondary finger to the left of the primary, the cursor
appears to the left of and above the primary finger.
Dual Finger Midpoint

To provide both variable offset and enable finer control of
the cursor speed, we have designed the Dual Finger
Midpoint technique. This technique is triggered by placing

Figure 3. Dual Finger Midpoint technique positions
the cursor at exactly the halfway point between thetwo fingers, giving the user both a cursor offset aswell as a variable reduction of cursor speed.

the secondary finger on the surface. The cursor is thenoffset to the midpoint between the primary and thesecondary finger. A similar behavior occurs on any resistive
touchpad that places the pointer at the midpoint of all
touches (e.g., SMART Board Interactive Whiteboard [1]).
While both fingers are in contact, moving either or both
fingers controls the movement of the cursor. Clicking is still
performed only by the primary finger. This techniqueallows for variable reductions in cursor speed: when both
fingers are moving in the same direction and the same
speed, the cursor follows with the same speed, while when
only one finger is moving, the cursor moves with half the
speed of that finger.
While the Dual Finger Midpoint technique was very
appealing to our initial testers and very simple to master, it
did not provide enough assistance for selecting the smallest
targets (2 pixels or less). At best, this method reduces thefinger speed by a factor of 2 which yields good results formost targets; but it does not provide enough control for thesmallest targets. An additional shortcoming of thistechnique is that not all locations on the screen are equallyaccessible. For example, screen corners are not accessibleusing midpoint selection. Consequently, the utility of thistechnique is somewhat limited by the fact that in today’suser interfaces small targets often are located in the cornersof the screen.
Dual Finger Stretch

Inspired by the strong performance of ZoomPointing
technique [2], we designed a Dual Finger Stretch techniquethat allows the user to adaptively scale a portion of thescreen with the secondary finger while the primary finger
performs the selection. To allow for simultaneous“stretching” and selection, the primary finger provides theinitial anchor location around which the user interface isscaled, while the secondary finger identifies the corner ofthe square area which will be scaled. By moving thesecondary finger closer or further away from the primary
finger, the square stretching area is reduced or expanded as
illustrated in Figure 4. Lifting the secondary finger from thetable resets the interface to its default un-stretched state.
Upon this reset, the cursor is offset with respect to theprimary finger and is placed where it was located in thestretched state. The cursor offset is reset when all fingers
are removed from the table. The extent of control-display
ratio manipulation depends on two physical limits: theFigure 4. Dual Finger Stretch technique adaptively
scales the user interface: a) The secondary finger
specifies the square zooming area centered at the
primary finger’s location, b) Primary finger performs
precise selection while, simultaneously, the secondary
finger adjusts the level of magnification.

Figure 5. Dual Finger X-Menu enables the user toadjust the cursor speed by crossing over a particulararea of the on-screen menu. Freeze mode is selected,
making the cursor completely immobile.

closest perceptible distance between user’s fingers and thelargest diagonal of the screen. For most common midscreen manipulations, Dual Finger Stretch enables controldisplay ratios roughly up to 10. By allowing clutching andrepeated zooming, it may be possible to further increase thisratio.
The Dual Finger Stretch technique has several advantages
over the ZoomPointing technique primarily due to the dual
finger design. First, zooming and selection are notdecoupled into two separate actions. Instead they canhappen concurrently which results in a fluid interaction.
Second, the interface scales in all directions from theoriginal primary finger’s location. This provides animportant advantage over traditional rectangle selection
where the two points specify the diagonal corners of thezooming rectangle (also known as bounding box zoom).
With the rectangle selection, the user tends to place theprimary finger off target in order to “capture” the target in
the zoomed area, while with Dual Finger Stretch, the user
places the primary finger directly on target and theinterfaces scales underneath in all directions. Placing thefinger off-target requires the user’s primary finger totraverse an increased distance to perform final selection
because the target will appear to move away from the finger
as the zoom level increases. By encouraging placement of
the primary finger as close to the target as possible, theeventual distance that this finger will need to traverse toacquire the target is minimized.
Dual Finger X-Menu

To allow users to adaptively adjust the control-display ratio
as well as obtain cursor offset while looking at an unzoomed user interface, we have designed the Dual Finger
X-Menu widget. This circular menu is invoked whenever
the secondary finger establishes contact with the surface. It
is positioned so that the finger is located at its center. The
user can select a particular assistance mode by moving thesecondary finger to any of the desired regions of the menu
(Figure 5). Dual Finger X-Menu has six selection areas

Figure 6. Dual Finger X-Menu contains four selection
areas for cursor speed control (normal, slow 4x, slow 10x
and freeze), and two toggle areas (snap and magnify).
Magnify mode presents an integrated magnification
widget in the middle of the menu, while Snap mode
removes the current cursor offset.

shown in Figure 6. Four areas control the relative speed of
the cursor: normal, slow 4X, slow 10X, and freeze. Normal
mode moves the cursor with the same speed as the primary
finger; the two slow modes reduce the speed of the cursor
by a factor of 4 and 10 respectively, while freeze mode
“freezes” the cursor in place, disabling any cursor
movement.
In preliminary experiments, we found that the ability tocompletely stop the cursor from moving has two benefits.
First, by freezing the cursor, the user can quickly and easily
establish a desired cursor offset. This is accomplished byfreezing the cursor temporarily, moving the finger toachieve the desired offset, and then unfreezing the cursor
again. Second, when selecting very small targets, evensmall amounts of noise can cause an error. Such noise canbe due to device tracking errors, tremor in the user’s hand,or noise due to the clicking motion. By freezing the cursor
in place, the user can ensure that the desired selection is
successful even in very noisy conditions.
The left two areas on the crossing menu invoke two helper
modes: “snap” and “magnify”. When snapping is triggered,
the cursor offset (if any) is removed and the cursor snaps

a)

a)

b)

c)

Figure 7. Cursor notification widget signals thecurrent amount of cursor speed reduction: a) 4x
reduction, b) 10x reduction, and c) frozen cursor.

back to the current location of the primary finger. Thismode is useful in repositioning the cursor in the slow
movement modes because it is easy to run out of tracked
screen space when using the slow cursor modes. Magnify
mode presents a small magnification area in the middle ofthe crossing menu that shows the enlarged area under the
cursor. The magnification factor is fixed at 2X. This modeis particularly useful when the primary finger overlaps the
cursor. In this case the magnified image acts as a lensshowing the portion of the interface obstructed by the
primary finger. A simple cursor notification widget displays
which cursor speed level is currently selected, without
requiring the user to refer back to the menu. The behaviorof this notification widget can be seen in Figure 7.
Dual Finger X-Menu is not operated by clicking, but rather
by “crossing” the finger into a particular area, whichenables more experienced users to activate modes bysimply performing quick strokes in a particular direction.
With practice, this selection can be made without looking,and could therefore allow for an expert mode in which themenu could be completely hidden from the user. Removing
the secondary finger from the surface will cause the menu
to disappear.
Dual Finger Slider

Encouraged by the possibilities of the different interaction
modes of Dual Finger X-Menu and the simplicity of Dual
Finger Midpoint, we developed the Dual Finger Slider
technique, which incorporates the menu’s most usefulfeatures, but simplifies and streamlines the overall
interaction (Figure 8). Given that two finger interactions area very natural way of specifying distance, we have designed
this interaction using the distance between fingers to switch
between cursor speed reduction modes. This technique does
not present an on-screen widget to the user. Instead, it relies
completely on the user’s ability to gauge the spatial
relationship between their fingers. The same cursor
notification widget (Figure 7) is used to signal the cursor
speed to the user.
Moving the secondary finger towards the primary finger
reduces the cursor speed in 3 discrete steps. This allows forthe same reductions in cursor speed that is available in Dual
Finger X-Menu: normal, slow 4X, slow 10X, and freeze.
Moving the secondary finger away from the primary
increases the speed up to the normal speed. Continuing to

b)

c)

d)

Figure 8. Dual Finger Slider – the right finger(primary) controls the cursor, the left finger(secondary) is invoking the invisible slider; speed
reductions modes are achieved by moving the fingers
closer together: a) normal, b) slow 4x, c) slow 10x, d)frozen cursor mode.

move the fingers apart triggers a “snap” which warps the
cursor back to the primary finger’s location. Snapping issignaled by a distinct sound effect. The distance that thesecondary finger traverses in switching speed reduction
modes is predefined and is not dependent on the distance
between the fingers. The modes are remembered even after
the user lifts the secondary finger which allows forclutching in the interaction.
PROTOTYPE MULTI-TOUCH TABLETOP DISPLAY

Our interaction techniques are designed for present andfuture multi-touch screens, and to some extent, they are
device independent. However, we have developed them ona prototype multi-touch tabletop display to facilitate
research and user studies (see Figure 9). The prototype uses
a diffuse screen coupled with an infrared camera andcomputer vision algorithm to detect contacts on the tabletop
surface. The screen is back-projected with the projector
integrated in the base of the table below the screen. Our
display uses the infra-red light spectrum for contact
detection while all projection is done in the visible

targets alternated between trials. The users were instructed
to click on the current green target as fast and as accurately
as possible. We recorded both movement times and error
rates, but we analyzed completion times only forsuccessfully completed trials. We had hypothesized that thesmallest targets might not be reliably selectable by all thetechniques tested and therefore were more interested in theimpact of our techniques on the reduction of error rate, thanthe completion time.

Figure 9. Our multi-touch back-projected tabletop
display prototype uses an infra-red illuminant andcamera to detect contacts through a diffuse surface.

spectrum. This separation allows the computer visionalgorithms to ignore the projected display in order to see
only surface contacts. A similar approach is used in the
TouchLight [26] display system and elsewhere.
The screen resolution of our prototype multi-touch device is1024 x 768 (pixels), which, given the screen dimensions of
61 x 46 (cm), yields a pixel size of 0.6mm. The finger thatis about 1.5 cm wide covers about 25 screen pixels.
LABORATORY USER STUDY

To evaluate the Dual Finger Selection and SimPress
techniques, we conducted a user study that challenged theusers to select small and large targets using the various
techniques. Additionally, we were interested in how wellthese techniques perform on devices of very low precision.
Such devices include touch screens based on a small
number of sensing elements, touch screens based on noisy
computer vision processes, and many prototype researchsystems which do not achieve the precision of the mouse(e.g. see [25, 26]). Accordingly, in our experiments we
added synthetic noise to the sensing system describedabove, and systematically varied its magnitude.
Twelve paid participants (9 male and 3 female), ages 20–
40, participated in the experiment. All subjects were
frequent computer users. They had varying experience withthe touch screens, ranging from “monthly” use to “several
times a day”, with the average corresponding to “weekly”
use. All subjects used their right hand as their dominanthand. Eleven subjects identified themselves as righthanded. The single left-handed subject preferred using theirright hand for mouse operation and chose to use the righthand as the dominant pointing hand in the experiments. The
subjects were pre-screened for color blindness.
The subjects were asked to perform a simple reciprocal
target selection task, with square targets of varying widths,
separated by a fixed distance of 100 pixels. This task isloosely based on the Fitts’ Law target acquisition task, but
without the variation of distance. The task involved clicking
on a green square target that was surrounded by a green
circle. The other (inactive) target was colored red and the

The experiment consisted of two parts: an evaluation of the
SimPress technique and a comparative evaluation of thefour dual finger selection techniques under varying amounts
of noise. Both used the same testing infrastructure topresent targets to the user, measure user performance andlog all experimental data. In addition, the users completed a
post-experiment user preference questionnaire.
Part One: SimPress Clicking

We wanted to determine the performance of SimPress
clicking technique to obtain a baseline measure of theminimal target width that is possible to select reliably
without additional assistance. An additional motivation was
to ensure that our subjects mastered and were comfortableusing SimPress, since we required them to use it throughout
later experiments. Our subjects were first given an
introduction to the SimPress technique and then allowed toperform 1 practice run before the actual experiment.
A within-subjects, repeated measures design was usedconsisting of 5 target widths (1, 2, 4, 8, and 16 pixels). The
widths were chosen to represent the range of smallest
available targets in a typical GUI. For example, the smaller
toolbar buttons tend to be between 15 and 20 pixels wide,
while the resize handles are sometimes less than 5 pixels
wide. The experiment consisted of 5 sets (1 set per width)of 5 trials each, for a total of 25 trials per user. The order ofthe sets was randomized across users.
Our hypothesis was that the users would be able to reliably
select only the largest of our small targets (16 pixels) andthat the finger’s occlusion of the target and the small
amount of noise still present while clicking would make theselection of other target sizes difficult.
Results

We performed a repeated measures ANOVA on the meanerror rate data and found the significant main effect withtarget width (F(4,44)=62.598, p<0.001). The data aresummarized in Figure 10. Paired samples t-tests show no
significant differences between the user’s performance with
8 and 16 pixel targets. A significance difference inperformance is shown between 2 and 4 pixel targets(t(11)=3.95, p=0.002) and 4 and 8 pixel targets (t(11)=4.16,
p=0.002). The difference between 1 and 2 pixels is ofborderline significance (t(11)=2.41, p=0.034).
Contrary to our hypothesis, we found that the thresholdtarget size, below which the SimPress technique is notPercent of Trials ± SEM

100
90
80
70
60
50
40
30
20
10
0

trials resulting in a total of 288 trials per user. To eliminate
the effects of switching selection strategies (for example
deciding to use a different cursor speed reduction for adifferent target size) we discarded the first trial in eachblock. All our techniques were modified to completely reset
after each click in order to ensure the same starting state forall trials.

reliable alone, is around 8 pixels. These results show that
SimPress is a viable option for use for most general
selection tasks in the current user interface.

Our main hypothesis was that techniques that increase thecontrol/display ratio lessen the impact of the input noise.
Therefore, Stretch, X-Menu and Slider should be lessaffected by the increase in noise, than the Offset technique.
The second hypothesis was that Slider would perform better
than X-Menu since the Slider is controlled by the natural bimanual way of specifying spatial extent (finger distance),
rather that the independent finger actions in X-Menu.

Part Two: Comparison of Four Dual Finger Techniques

Results

2

4

8

16

Target Width (pixel)

Figure 10. Mean error rate (%) using the SimPress
technique alone without any assistance.

For the second part of the experiment, we tested theperformance of our Dual Finger Selection by comparing thethree most versatile techniques (Stretch, X-Menu andSlider) and the Offset technique. By providing no additional
assistance other than the cursor offset, the Offset techniqueserved as a baseline. Even though the Midpoint techniquereceived very positive response from our initial testers, thistechnique was not included due to the relatively small
assistance that it offered in selection (the maximum
reduction of the cursor speed was a factor of 2) and lack ofequal accessibility to all screen locations.
Additionally, we were interested in how our techniques
would perform in the presence of noisy input. We note thatmany touch screen devices provide noisier input thanstandard relative pointing devices such as a mouse. This isparticularly true of a whole class of touch screen devices
that depend on the video signal for their touch recognition.
In addition to noise in the video stream, such devices often
require that the video signal is up-sampled to match thescreen’s resolution. This up-sampling introduces additional
sampling noise. In order to test how our techniques deal
with increased noise, we added Gaussian noise to theposition of each tracked finger, creating three noise levels:
low (no additional noise), medium (Gaussian noise with
σ=0.5), and high (Gaussian noise with σ=2).
While the noise can be reduced with a use of a filter(Kalman filter being the most commonly used), thissolution either results in a slight cursor lag or overshootwhen the finger's velocity abruptly changes, as is the casewith any start or stop of the finger. We believe that there isa benefit to having interaction techniques that adaptively
allow the user to reduce the noise when so desired, leaving
the noisy, but lag-free, input otherwise. By manipulating
the control/display ratio, Stretch, X-Menu, and Slider
implicitly allowed the reduction of the input noise as well.
Our study followed a within subjects design that tested 3
noise levels, 4 techniques, and 4 target widths (1, 2, 4, and8 pixels) per block. Within each block, the user performed 6

We performed a 3 (Noise) x 4 (Technique) x 4 (Width)
repeated measures ANOVA on the mean error rate data andfound the significant main effects across all conditions. As
expected, noise had a significant main effect on the error
rate (F(2,22)=20.24, p<0.001). This confirmed that moreerrors were committed in the higher noise levels.
Significant main effects were also present for width(F(3,33)=150.4, p<0.001) and technique (F(3,33)=169.138,
p<0.001). Paired samples t-tests show that the Offset
technique created significantly more errors than the rest(t(11)=14.298, p<0.001), while Stretch was better than the XMenu or Slider (t(11)=2.864, p=0.015). No significant
difference was found in the error rate between X-Menu andSlider techniques.
The interaction of technique and width (F(9,99)=29.473,
p<0.001, Figure 12) is interesting as it shows that ourassistive techniques (Slider, X-Menu, and Stretch) all
performed exceptionally well (less than 5% error rate) in allnoise conditions for targets 2 pixels or larger (no statisticaldifferences between techniques). For the smallest target (1pixel), Stretch outperformed X-Menu and Slider (withborderline significance t(11)=2.64, p=0.023). The interactionof noise and technique was also significant (F(6,66)=8.025,
p<0.001, Figure 11). While the increase of noise greatly
degraded performance of the Offset technique, the other 3
techniques show no statistically significant effects to thevarious noise levels. This confirmed our main hypothesislowmediumhigh70
ErrorRate (%) ± SEM

1

60
50
40
30
20
10
0
Ofset

X-MenuSliderStretchFigure 11. Interaction of Noise x Technique graph forerror rate (%).

X-Menu

SliderStretchOffset

100
90
80
70
60
50
40
30
20
10
0

X-MenuSliderStretch

7

W-1

W-2

W-4

W-8

Figure 12. Interaction of Technique x Width graph forerror rate (%).

that users are able to lessen the impact of noise and low
precision by using techniques that allow for control-display
ratio adjustments.
Due to the dramatically high number of errors committed
by our users using the Offset technique, our data contains
several blocks without a single successful trial (all in 1
pixel width condition). While this prevented us fromperforming a repeated measures ANOVA on movement
times, we present the informal analysis of median
movement time values for blocks for which we have data.
Median times were chosen to correct for the typicalskewing due to reaction time data. This also removed theinfluence of any outliers in the data. Aggregating the
movement times across all noise levels and all target
widths, the Stretch technique is on average 1s faster than
Slider (t(11)=5.011, p<0.001). There do not appear to bestatistical differences in the performance times of Slider and
X-Menu. This failed to confirm our second hypothesis that
Slider would outperform X-Menu. Offset’s performancetimes were comparable to other techniques, indicating thatusers did not believe that spending more time on targeting
would yield more precise targeting with Offset technique.
Figure 13 shows the performance of techniques with respect
to target width. The data shows a general trend of moretime being spent on targeting smaller targets.
Subjective Evaluation

The users filled out a post-experiment questionnaire rating
their experience with four techniques on a 5 point Likert
scale (1 being most negative and 5 being most positive)
They were asked to comment on the following categories:
mental effort, learning time, hand fatigue, enjoyment, andperformance in low, medium and high conditions.
Overall, techniques received significantly different results(F(3,33)=45.9, p<0.001). X-Menu required the most mental
effort (average score of 2.88), and the longest time to learn(average score of 2.09). Data shows no significant statisticaldifferences between techniques with respect to handfatigue. Stretching was the most enjoyable (average scoreof 4.12), followed closely by Slider technique (averagescore of 4.08). We also asked users to rate their overall
preference for the technique for selecting small targets.

Movement Time (s) ± SEM

Error Rate (%) ± SEM

Offset

6
5
4
3
2
1
0
W-1

W-2

W-4

W-8

Figure 13. Mean performance time (s) with respect totarget widths. (Notice: time data is not shown forOffset technique at 1 pixel due to lack of successfullycompleted trials.)

Stretch was the most preferred (7 subjects), followed bySlider (4 subjects), while only one user preferred X-Menu.
DISCUSSION AND CONCLUSIONSOut of the four compared techniques, the top performer andmost preferred technique, Stretch, was the only one that did
not provide a cursor offset. This clearly demonstrated thatthe benefit of increased target size successfullycompensated for the fingertip occlusion factor. The datafrom this experiment is consistent with the results from astudy by Albinsson and Zhai [2] which also showed that
their baseline zooming technique outperformed on-screen
widgets that provided cursor speed control.
We feel that Dual Finger Stretch is a simple and powerfulinteraction that utilizes the distance between fingers in avery natural way. However, in many applications, scaling
may have an undesired effect of losing overview of theinterface. Therefore, we were very pleased with the strong
performance of Slider and X-Menu, which provided
comparable error rates with a small time penalty of about 1s
when compared to Stretch. In addition, as our subjects’
written comments point out, those techniques strongly
benefit by the ability to freeze the cursor. As one subject
describes, freezing the cursor is a functional equivalent topresenting a user-controlled “are you sure?” dialogue forclicking which enables the user to select a particular point
without risk of mistake, or go back and re-target. This wasparticularly useful with higher noise levels. Experienceshould also substantially improve our Slider and X-Menu
because zooming is a very familiar interaction for mostusers. As such, it might have an unfair advantage whencompared to other speed-controlling techniques.
The SimPress clicking technique exceeded our performance
expectations. This enables the novice users to reliably clickon targets that are as small as 8 pixels. We believe that withpractice and more thorough calibration, this threshold could
be further reduced. Some future work on stabilization isneeded in order to completely remove the remaining noise

from clicking. An additional SimPress modification was
implemented, but not tested, permitting the user to rapidly
click on targets without requiring the rocking motion. This
timer solution generates a click event if the contact waspresent on the surface for less than 0.4 s. This allowed thesimple selection of large targets to remain as direct as
possible while more complex interactions, such as drag anddrop, can be performed using the SimPress technique.
Our study results show that Dual Finger Selections present
viable solutions for increasing precision and accuracy in asmall target selection task. They are designed to be used onmost multi-touch screens, perform well with the increase of
input noise, and fully utilize the benefits of dual finger
interactions. Overall, these techniques provide a palette ofinteractions from which the user may chose depending on
the application.
ACKNOWLEDGMENTS

We would like to thank Ed Cutrell, Ken Hinckley, andSteven Feiner, for their support and helpful comments.
REFERENCES

1. SMART Technologies, http://www.smarttech.com.
2. Albinsson, P.A. and Zhai, S. High Precision Touch
Screen Interaction. Proc. CHI '03, 2003, pp. 105-112.
3. Baudisch, P., Cutrell, E., Hinckley, K. and Eversole, A.
Snap-and-go: Helping Users Align Objects Without theModality of Traditional Snapping. Proc. CHI '05, 2005,
ACM Press, pp. 301-310.
4. Benko, H., Ishak, E. and Feiner, S. Cross-Dimensional
Gestural Interaction Techniques for Hybrid Immersive
Environments. Proc. IEEE VR '05, 2005, pp. 209-216.
5. Bier, E.A., Stone, M.C., Pier, K., Buxton, W. and
DeRose, T. Toolglass and Magic Lenses: The SeeThrough Interface. Proc. ACM SIGGRAPH '93, 1993,
pp. 73−80.
6. Blanch, R., Guiard, Y. and Beaudouin-Lafon, M.
Semantic Pointing: Improving Target Acquisition with
Control-Display Ratio Adaptation. Proc. CHI '04, 2004,
ACM Press, pp. 519-526.
7. Butler, C.G. and Amant, R.S. HabilisDraw DT: A
Bimanual Tool-Based Direct Manipulation Drawing
Environment. Proc. CHI '04 Extended Abstracts, 2004,
pp. 1301-1304.
8. Buxton, W. Issues and Techniques in Touch-Sensitive
Tablet Input. Proc. ACM SIGGRAPH '85, 1985, pp.
215-224.
9. Buxton, W. and Myers, B. A Study in Two-Handed
Input. Proc. CHI '86, 1986, pp. 321-326.
10. Cutler, L.D., Fröhlich, B. and Hanrahan, P. TwoHanded Direct Manipulation on the Responsive
Workbench. Proc. Symposium on Interactive 3D
Graphics, 1997, pp. 107-114.
11. Dietz, P. and Lehigh, D. DiamondTouch: a Multi-User
Touch Technology. Proc. UIST '01, 2001, pp. 219−226.

12. Forlines, C. and Shen, C. DTLens: Multi-User Tabletop
Spatial Data Exploration. Proc. UIST '05, 2005, ACM
Press, pp. 119-122.
13. Grossman, T., Wigdor, D. and Balakrishnan, R. MultiFinger Gestural Interaction with 3D Volumetric
Displays. Proc. UIST '04, 2004, pp. 61-70.
14. Han, J.Y. Low-Cost Multi-touch Sensing ThroughFrustrated Total Internal Reflection. Proc. UIST '05,
2005, ACM Press, pp. 115-118.
15. Hinckley, K., Pausch, R., Proffitt, D. and Kassell, N.F.
Two-Handed Virtual Manipulation. ACM Transactions
on Computer-Human Interaction, 5 (3). pp. 260-302.
16. Kabbash, P., Buxton, W. and Sellen, A. Two-Handed
Input in a Compound Task. Proc. CHI '94, 1994, ACM
Press, pp. 417-423.
17. MacKenzie, I.S. and Oniszczak, A. A Comparison ofThree Selection Techniques for Touchpads. Proc. CHI'98, 1998, ACM Press, pp. 336-343.
18. Malik, S., Ranjan, A. and Balakrishnan, R. Interactingwith Large Displays from a Distance with VisionTracked Multi-Finger Gestural Input. Proc. UIST '05,
2005, ACM Press, pp. 43-52.
19. Olwal, A. and Feiner, S. Rubbing the Fisheye: Precise
Touch-Screen Interaction with Gestures and Fisheye
Views. Conference Supplement of UIST '03. pp. 83-84.
20. Patten, J., Ishii, H., Hines, J. and Pangaro, G.
Sensetable: A Wireless Object Tracking Platform for
Tangible User Interfaces. Proc. CHI '01, 2001, pp. 253260.
21. Potter, R.L., Weldon, L.J. and Shneiderman, B.
Improving the Accuracy of Touchscreens: An
Experimental Evaluation of Three Strategies. Proc.
Proc. CHI '88, 1988, pp. 27-32.
22. Rekimoto, J. SmartSkin: an Infrastructure for Free-hand
Manipulation on Interactive Surfaces. Proc. CHI '02,
2002, pp. 113−120.
23. Ren, X. and Moriya, S. Improving Selection
Performance on Pen-Based Systems: A Study of PenInput Interaction for Selection Tasks. ACM
Transactions on Computer Human Interaction 7(3). pp.
384-416.
24. Sears, A. and Shneiderman, B. High Precision
Touchscreens: Design Strategies and Comparisons witha Mouse. International Journal of Man-Machine
Studies, 43 (4). pp. 593-613.
25. Wilson, A. PlayAnywhere: A Compact Tabletop
Computer Vision System. Proc. UIST '05, 2005, ACM
Press, pp. 83-92.
26. Wilson, A. TouchLight: An Imaging Touch Screen and
Display for Gesture-Based Interaction. Proc. ICMI '04,
2004, pp. 69-76.
27. Wu, M. and Balakrishnan, R. Multi-Finger and Whole
Hand Gestural Interaction Techniques for Multi-User
Tabletop Displays. Proc. UIST '03, 2003, pp. 193-202.