1

Touch-based Target Selection for MobileInteraction

Technical Report HMT-11-01

Sean T. Hayes, Eli R. Hooten, Julie A. Adamsψ

Abstract

Smart phone and slate based mobile devices have changed the way individuals interact withcomputers. Users can complete tasks efficiently “on the go” using a mobile device’s touch screen.Therefore, it is important to investigate touch interaction as it pertains to a mobile (i.e., walking)user. A user evaluation was conducted using a slate PC to present a target selection task within amap-based interface. Participants interacted with the mobile device while seated or while walkingin an uncontrolled environment. Results indicated that mobile target selection error is significantlyhigher for the mobile user. Effective widths of targets must also be significantly larger in the mobilecase and a recommended model for target width is provided. Results also indicate that finger-basedinteraction differs from thumb-based interaction on a mobile device.

I. Introduction

THE introduction of smart phone and slate personal computer technology has changed the waythat individuals complete computing tasks. Smart phone and slate devices allow users to

perform computing tasks virtually anywhere. According to market research, smart phone sales arepredicted to eclipse the sale of personal computers (e.g., desktop PCs, notebooks, etc.) by 2012 [1].Likewise, tablet PC sales are expected to triple by the end of 2011 [2].

Touch-based interaction methods are routinely used as the predominant input method for smartphone and slate form factor mobile devices. Numerous studies have attempted to understand touchinteraction for mobile devices with small screens [3–5]. However, due to the increasing popularityof slate devices, it is important to study touch interaction in the context of larger, slate form factordevices.

Walking plays a key factor in mobile device usage. Since slates are inherently portable, they areoften used “on the go” with the user performing computing tasks while walking. Several studiesof mobile device interaction while walking have been conducted [6–8]. However, these studies areperformed using mobile phones and/or personal digital assistants, which can employ hard buttons,a stylus, a touch screen, or a combination thereof. The mobile devices evaluated are small in sizeand interaction is usually facilitated using the thumb. It is unknown if the findings of thumb-basedmobile interaction studies are applicable to mobile devices with larger touch screens.

The target selection task is often used for evaluating the usability of mobile devices [5, 6, 8, 9].The target selection task places targets on the screen and tasks the user with selecting the targets.Targets are usually static in nature and appear on the screen at some offset from the screen’scenter point. While target selection is important, mobile devices are not solely used for selectingstatic targets. Many mobile device applications (e.g., web browsers, map-based applications, andgames) require a user to scroll the interface before selecting an on screen element. Therefore, it

ψ Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USAemail: {sean.t.hayes, eli.r.hooten, julie.a.adams}@vanderbilt.edu

Hayes et al.: HMT-11-01 2

is worthwhile to study mobile target selection within scrollable interfaces in order to determine ifscrolling (i.e., panning) the interface negatively affects target selection.

This paper evaluates the fundamental interactions that drive touch screen interaction on largermobile devices (e.g., slate-based form factors). The impact of panning on target selection tasks isalso explored through embedding the target selection task in a map-based interface that must bepanned in order to search for targets. Through coupling target selection with panning, the resultingfindings are more indicative of real-world applications.

II. Background

A. Fitts’ Law, Throughput, and Effective Width

Fitts’ law is a model of human movement that predicts the time required to move a selector(e.g., mouse, finger, stylus, etc.) from some starting position to a target area. Fitts’ law describesthe fundamental relationship between speed and accuracy when selecting targets. It is commonlyrepresented in the Shannon formulation [10] as follows:

T = a+ b log2

(1 +

D

W

), (1)

where T is the average time to complete the movement and a and b are constants typically derivedfrom experimental data. D is the distance from the start point to the target’s center and W is targetwidth. The logarithmic portion of Equation 1, measured in bits, is called the index of difficulty(ID).

Effective width, We, is an important adjustment to Fitts’ law, which accounts for the varianceof hits upon the target [10]. We normalizes a target’s actual width to align with how a participantactually performed when striking the target. Based on the standard deviation of the data, We is ameasure of precision.

Mackenzie represented effective width in the one dimensional case [10]; however, later work byMurata extended effective width to two-dimensional target selection tasks [11]. Murata extendedWe as follows:

We =

√−4 ln(α)

(σ21 + σ2

2 ±√

(σ21 − σ2

2)2 + 4σ21σ

22r

2

), (2)

where α is the acceptable error percentage (usually α = 4%), σ1 and σ2 are the standard deviationin each of the two dimensions, and r is the Pearson correlation coefficient. Equation 2 is based on atwo-dimensional, normal joint probability distribution and effectively considers the impact of bothdimensions on the target selection task. Note that Equation 2 produces two values, one effectivewidth per dimension.

Throughput (i.e., Index of Performance) measures the amount of information expressed per unitof time, usually reported in bits per second [10]. Zhai determined that TP is dependent upon themean ID for the tested tasks [12], and asserts that TP is an acceptable metric for comparison ifthe experiment maintains the mean ID across comparisons. TP was calculated as follows:

TP =ID

T, (3)

which was applied after determining that the experimental data agreed with the defined require-ments [12].

Hayes et al.: HMT-11-01 3

B. Related Work

It can be difficult to conduct real-world mobile interaction studies due to external distractions asa participant moves throughout an uncontrolled environment [13]. Laboratory based mobile inter-action studies, which usually task the participant with performing a computing task while walkingon a treadmill or around a short, controlled course, also have limitations. Barnard et al. studiedthe limitations and advantages of using a treadmill versus a controlled path for mobile interactionstudies [14]. They found that a treadmill set at a comfortable walking speed provides an easilycontrollable scientific setup. Objective measures obtained from treadmill studies appeared to be inagreement with similar measures taken from controlled walking studies. Barnard et al. concludedthat treadmill studies do not adequately capture the attentional demands that are present duringreal-world mobile interaction scenarios. It was determined that a treadmill provides an adequateenvironment for basic research, but using a controlled path is more appropriate for comparisonstudies.

Kjeldskov and Stage studied the effects of six different environmental conditions on mobile inter-action [7]. The six different conditions included a seated condition, walking on a treadmill at varyingand constant speeds, walking through a test course at varying and constant speeds, and walkingthrough a pedestrian street. The pedestrian street served as the uncontrolled free-movement sce-nario, which embodies a more typical usage situation. Kjeldskov and Stage mentioned that, duringthe pedestrian street trial, it was difficult to obtain objective data through external observation.They concluded that their pedestrian street trial was probably not an adequate representation ofa real-world scenario because two evaluators walked alongside each participant. Other pedestrians,seeing the evaluators, were aware of the study and actively avoided the participant and evaluators.

Kjeldskov and Stage’s test track experiment required participants to perform mobile interactionwhile following behind an evaluator on a dynamically changing course. The authors doubted thevalidity of this approach as often participants watched the leading evaluator from the corner of theireye while interacting with a mobile device [7]. Therefore, the participant did not need to devotemuch attention to navigating the test track.

Based on Kjeldskov and Stage’s evaluation, recommendations for conducting more valid real-worlduser evaluations can be made. Evaluators should not walk alongside participants in uncontrolled,free-movement trials. Doing so may alert other individuals in the environment to the test and alterthe response of the individual to the participant. A leader should also be avoided. When using aleader, the participant may devote too little attention to the environment and rely too heavily onthe leader’s guidance, simplifying the mobile task. A more accurate representation of a real-worldscenario places the participant at the forefront, walking alone, followed at a safe distance by a singleevaluator. Of course, appropriate precautions are necessary to insure the participant’s safety.

Schedlbauer and Heines evaluated the mean selection times and error rates for participants per-forming a target selection task while standing and walking [15]. Selection tasks were performedusing a stylus in both conditions. The authors determined that Fitts’ law was a robust predictorof movement time to a target in both conditions. They reported an R2 = 0.85 for walking and anR2 = 0.89 for standing. A significant difference in target selection time was not found between con-ditions, which contradicts earlier findings by Chamberlain and Kalawasky [16]. The authors pointout that Chamberlain and Kawalsky’s walking task was more difficult. Schedlbauer and Heines alsoreported, with significance, that the walking condition took more time and was more error prone.

Lin et al. reported that Fitts’ law was effective when performing target selection using a stylusduring walking tasks that occur both on a treadmill and in uncontrolled environments [17]. Thewalking task involved following a line taped to the floor through an obstacle course that was thought

Hayes et al.: HMT-11-01 4

to require a great deal of the participants’ attention. Lin et al. found that walking through theobstacle course caused significantly more error when compared to both the seated and treadmillwalking task. Lin et al. also reported that, when using a stylus while performing free-style walking,participants accurately selected a target with a 6.4 mm diameter 90% of the time while walking at anaverage speed of 0.625 meters per second. Lin et al. also found that target selection accuracy differedsignificantly when the participant walked on a controlled course versus walking on a treadmill.

It is debatable whether or not the findings from stylus-based mobile interaction studies are rep-resentative of touch interaction using a finger. For one, the finger is larger than a stylus and hasdifficulties performing precise selection (i.e., the “fat finger problem”). Therefore, we believe thatthe findings of Lin et al. concerning minimum target size are not representative of touch interactionusing a finger.

Touch-based mobile interaction studies tend to focus on thumb-based interaction for target selec-tion tasks. The small screen size of many mobile devices (e.g., smart phones) makes it possible toselect any point on the device’s screen using a thumb, thus making thumb-based selection popularfor small mobile devices. Parhi, Karlson, and Bederson found that for single selection tasks, suchas selecting one button using the thumb, required a target size of 9.2mm [5]. Apple’s iOS HumanInterface Guidelines state that a target size of 6.74mm results in a suitable compromise betweenerror rate and screen size [18].

Bergstrom-Lehtovirta, Oulasvirta, and Brewster tasked participants with performing target selec-tion while walking on a treadmill at varying speeds [6]. The authors determined, with significance,that target selection accuracy decreases from 100% (obtained when standing still) to 89% whenwalking at 20% of preferred walking speed. Bergstrom-Lehtovirta et al. concluded that all typesof walking, no matter how slowly, have a noticeable negative impact on target selection accuracyand selection time, which agrees with previous studies [17, 19, 20]. Of these previous studies, twowere stylus-based [17, 20] and one was touch-based [19], but interaction was primarily thumb-based.Bergstrom-Lehtovirta et al. tasked participants with selecting targets bimanually, where the devicewas held in one hand and a finger on the opposite hand selected on-screen targets. Therefore, theresults obtained by Bergstrom-Lehtovirta et al. may be applicable to larger mobile devices, such asslate PCs.

Henze, Rukzio, and Boll studied selection using a mobile device by publishing a target selectiongame to the Android Market [21]. Henze et al. found that touch events are systematically skewedtoward a position in the screen’s lower right corner. The authors hypothesized that the skewingwas due to a majority of users employing thumb-based interaction to select the on-screen targets.However, since the experiment was uncontrolled, it is impossible to know how many participantsused their thumb and how many interacted using alternative methods. Therefore, it is difficult toknow if the results generalize to larger screens that are used bimanually. Henze et al.’s data setmay have also included target selection data from larger Android-based slate devices. However, nodifferentiation was made between smart phone devices and slate devices.

Henze and Boll attempted to apply Fitts’ law to the data set generated from the experimentdescribed by Henze et al. [22]. Henze and Boll found a very weak correlation to Fitts’ law (R2 = .334)and an unrealistically high throughput regardless of how they filtered the data set. Once again, thestudy’s low internal validity makes it difficult to determine why Fitts’ law did not fit the data.

Based on a literature review, it appears that the most realistic mobile interaction results canbe obtained by performing a mobile study in an unconstrained environment. Even though uncon-strained environments are harder to control, testing mobile interaction in a free-movement scenariowith external factors may provide the most realistic data [14]. It is believed that stylus-based

Hayes et al.: HMT-11-01 5

Fig. 1: Four unselected targets and the indicator arrow.

mobile interaction will not be directly applicable to touch-based interaction on larger devices; how-ever, prior results provide a baseline for comparison [15, 17]. Bergstrom-Lehtovirta et al.’s work mayprove applicable to mobile slate devices, because target selection was accomplished using a finger,not the thumb [6]. Based on Henze et al.’s findings concerning skewing [21], it may be worthwhileto determine if such a skewing exists for slate devices, since the majority of slate device interactionsare difficult or impossible to perform unimanually using the thumb.

Despite the fact that slate mobile devices require bimanual interaction when being used whilewalking, interaction can be thumb-based. A user can hold the mobile device with two hands and usehis/her thumbs to interact with the left and right portions of the screen. Bimanual, thumb-basedinteraction on a slate device has been observed by industry [23]; however, if the slate device islarger than the span of the user’s two thumbs, all screen portions are not fully accessible, makingfinger-based interaction preferable for larger slate devices.

No studies were identified that require a user to pan an interface while performing a mobile targetselection task. It is believed that this style of interaction (scroll the interface to find an item ofinterest, and then select that item of interest) more accurately represents the behaviors exhibitedin many current mobile applications.

III. System Description

A target selection task was embedded in a map-based interface with a map depicting a moderatelypopulated urban setting. The interface permits scrolling via a two-finger pan gesture that requiresplacing two fingers on the screen and dragging the two fingers across the screen surface. Scrollingis inverted; a pan to the left causes the interface to scroll to the right and a pan up causes theinterface to scroll down. The map contains a large gray border, so that a user can scroll any pointof the map to any point on the screen. Thus, a target can be positioned at any point on the screenbefore being selected.

Target selection is facilitated through using a single finger to touch a target on-screen. Targets(see Figure 1) were circular so that the participants’ angle of approach to a target did not requireconsideration during analysis. Targets were 50 pixels (10.18 mm) in diameter, colored red, andbisected horizontally and vertically by 1 px wide black lines with a 1 px wide white border. The

Hayes et al.: HMT-11-01 6

5 px

1 mm(a) Seated

5 px

1 mm(b) Mobile

Fig. 2: Target hit points for both conditions represented on a target that has been drawn to scale.

intersection of the horizontal and vertical line indicated the target’s center. Targets also had adouble border of black and white to make them more salient. When a target was selected, its colorchanged from red to green, but remained visible on the screen.

Participants were notified of which target to select via an on-screen indicator. The indicatorpointed at the intended target until the intended target was selected by the participant and thenthe indicator moved to the next intended target. The indicator was a blue arrow with a black andwhite double border, as can be seen in Figure 1.

Targets were distributed over an area larger than the viewable area of the map. Over the course ofthe evaluation, a minimum of one panning gesture was required to complete the scenario. However,it is impossible to predict the maximum number of pans a participant will perform.

The interface logged all touch interactions. The time stamp of every pan gesture was recorded,as well as the pan gesture’s start and end positions. Each target’s hit point in screen coordinateswas time stamped and logged.

The interface ran on an ASUS EP121 slate personal computer [24] with a 12.1” screen, an IntelCore i5 processor and 4 GB of RAM, running Windows 7 Enterprise Edition. The interface wasdisplayed at a resolution of 1280× 800 px2 in a landscape orientation. The test apparatus includeda Garmin FR 20 pedometer and foot pod to measure walking distance and walking time.

IV. Experimental Method

A between subjects user evaluation was conducted. The independent variable was the mobilitycondition: Seated versus Mobile. Participants selected ten targets distributed on the screen. Thesame target placement was used for both conditions, and the interface showed the same map regionat startup for both conditions. The dependent variables were selection error, mean time to selecttargets, pan distance, and pan time. Objective metrics were the error between a participant’s hit

Hayes et al.: HMT-11-01 7

point on a target and that target’s center point, pan distances and times, total task completiontimes, and selection time per target. No subjective metrics were collected.

The Mobile condition required participants to walk the same path in an uncontrolled environ-ment. Participants walked straight ahead until they reached an intersection. Upon reaching theintersection, the experimenter indicated a direction for the participant to turn. Participants wereinformed of the direction in which to turn in enough time to make the turn without adjusting theirwalking speed or stopping altogether. The environment was the third floor of Featheringill Hall atVanderbilt University, which experiences a low to moderate level of foot traffic throughout the day.Participants walked approximately 8 feet in front of a single evaluator. The participant could besafely stopped verbally if he/she was in danger of colliding with another individual, a wall, furni-ture, etc. Participants cradled the slate in the non-dominant arm and selected targets with theirdominant hand. The participants were allowed to set their own walking pace when performing theMobile condition, and remained walking throughout the evaluation.

The Seated condition participants sat in a chair and cradled the interface in their non-dominantarm and used their dominant hand for target selection. Participants were instructed to select eachtarget as close to its center point as possible in both conditions. Participants in both conditionsselected targets using only the index finger.

A. Participants

The Seated trial included 30 participants, 17 of which were male, and 26 of which were righthanded. The median and mode participant age in the Seated condition was 25, the minimum agewas 19, and the maximum age was 39.

The Mobile trial included 33 participants, 19 of which were male, and 30 of which were righthanded. The median age was 24, the mode age was 19, the minimum age was 18, and the maximumage was 34. Participants walked an average of 32.77± 27.85 m. The large standard deviation islikely due to participants being allowed to walk at their own pace.

B. Evaluation Metrics

Based on the data obtained from the user evaluation, several metrics were developed. The firstis target selection error, which is defined as the magnitude from the center of a selected target tothe participant’s hit point on the target. The center of the target is considered the origin of apolar coordinate system. All distances were measured in pixels and converted to mm by obtaininga pixel to mm ratio based on the dimensions of the screen used for the evaluation. The ratio wasdetermined to be 0.2037mm

px. Since target selection error directly measures the distance of the data

set from the center point, it is a measure of accuracy.Effective width, We, values were obtained by applying Equation 2 to the standard deviations

of the x- and y-dimension offsets from each target’s center in millimeters. We was used in thecalculation of each ID. Throughput, TP , was measured per hit for each condition by applyingEquation 3.

Pan time and pan distance were also measured. Pan time is the amount of elapsed time from thestart of a single pan to the moment when a participant lifts his/her fingers from the screen, thusending the pan. Pan time is measured in milliseconds and pan distance in millimeters.

A search time metric was developed to quantify the time a participant spent searching for atarget. Search time is the elapsed time between selecting a target and the completion of a pandirectly before the next target is selected. Since a user can perform multiple panning gesturesbetween individual targets, multiple pans can comprise the search time metric. This metric is not

Hayes et al.: HMT-11-01 8

a perfect indication of the time a user spent searching for the next target to select, because a userspends some amount of time searching the screen for the next target after panning. However, thesearch time metric does provide insight into the amount of time spent searching for a target withina scrolling interface. Search distance, measured in millimeters, is the pan distance traveled whilesearching for the next target. It is the distance analogue to search time.

Two metrics were used to evaluate touch point skewness. Selection offset is defined as the targetselection offset for a particular region of the screen. Similar to Henze et al., the slate screen wassubdivided into equal sized rectangular regions [21]. 24 rectangular quadrants of equal dimensionwere created. These quadrants excluded any portion of the interface where a target cannot beselected (e.g., the map scroll bars, etc.), and each measured 40.45 x 36.55 mm. The total subdividedarea measured 242.71 x 146.19 mm. The selection offset was calculated for each quadrant todetermine if offset magnitude and direction for targets differed by screen region. Hit count perregion was also considered. Hit count was used to determine the region of the screen in whichparticipants frequently selected targets.

C. Hypotheses

Based on the findings from the literature review, the following hypotheses were generated:H1 Significantly more target selection error will occur for mobile interaction, and target effec-

tive widths will be larger for mobile interaction.H2 Mobile interaction will have longer task completion times.H3 The distribution of hit points on the screen will not agree with the results reported by

Henze et al. [21].We do not hypothesize as to how the hit points will be distributed. Since no literature was foundrelated to panning and its effects on a target selection task, we cannot hypothesize about the impactof panning.

V. Results

A. General Target Selection

All target selection data was compared between Seated and Mobile participants. There were 329total target selections in the Mobile condition and 299 in the Seated condition. One touch point felloutside its intended target in the Seated condition and three in the Mobile condition. The targethit points for both the Seated and Mobile conditions are shown in Figure 2.

The descriptive statistics for the Seated and Mobile condition are shown in Table I. The averageWe for each category in Table I, is obtained by taking the maximum We from the two produced byEquation 2. The maximum We was chosen over the minimum (which was used by Murata [11]),because the maximum We resulted in better R2 values than the minimum We when compared to thehit point data. The dimension used for the reported We is listed with the reported We’s standarddeviation in Table I.

Figure 2 depicts all hit points on all the targets for each condition. The target is drawn to scale,but hit points have been overemphasized to improve readability of the figure. Note that in theSeated condition, hit points are clustered closer to the center of the target, whereas a wider spreadon hit points is seen in the Mobile condition.

Since effective width is related directly to the variance of the data, the Brown-Forsythe Levene-type test was used to test for a significant difference in the variance of the data per condition. Thedifference between effective widths for the Seated and Mobile condition was significant, F (1,623) =16.00, p < 0.001. The difference between effective widths for males and females in the Mobile

Hayes et al.: HMT-11-01 9

Selection Error (mm)Condition Subjects Mean Std. Dev. We Std. Dev. r

Seated Overall 1.64 0.92 6.86 1.35 (y) -0.045Male 1.68 0.892 6.87 1.35 (y) -0.043

Female 1.61 0.943 6.88 1.35 (y) -0.048

Mobile Overall 2.21 1.29 8.67 1.70 (x) -0.034Male 1.91 1.12 9.62 1.89 (x) -0.027

Female 2.44 1.36 7.44 1.45 (y) -0.051

TABLE I: Target selection error descriptive statistics.

Condition From Count Mean Std. Dev We Std. Dev. r

Seated Pan 110 1.74 0.97 7.65 1.51 (y) -0.007Target 159 1.55 0.89 6.47 1.25 (x) -0.086

Mobile Pan 117 2.15 1.19 8.74 1.51 (y) 0.011Target 176 2.21 1.19 8.94 1.73 (x) -0.053

TABLE II: Pan distances descriptive statistics.

condition was also significant, F (1, 324) = 10.47, p < 0.005, but a significant difference was notfound between males and females for the Seated condition.

Given the Brown-Forsythe test results, a Welch’s t-test was used to compare the target selectionerror. The difference between the target selection error for the Seated and Mobile conditions wassignificant, t(622) = 6.36, p < 0.001, supporting H1. The difference between the target selectionerror for males and females in the Mobile condition was significant, t(323) = 3.8709,p < 0.001. Thedifference between the target selection error for males and females in the Seated condition was notsignificant.

A Fitts’ law regression was performed on the target selection data (see Table I). Hit points thatrequired panning were excluded, because the Fitts’ law model was designed to model only theselection portion of the task. 178 Mobile hit points and 152 Seated hit points did not requirepanning. The regression line was determined per condition with the targets’ physical widths andthen with their effective widths. In each case, R2 < 0.5, which is too low to consider Fitts’ law anadequate model for the data. A significant difference in TP when using the effective width wasdetermined between the Seated and Mobile conditions, t(328) = 12.89,p < 0.01. However, since theR2 of the Fitts’ law regression was low in both conditions, the significant TP result is dubious.

The average total runtime for the Mobile condition was 25.36±16.65s. The average total runtimefor the Seated condition was 20.74± 5.73s. A t-test comparison between the total average runtimeacross conditions was nearly significant, t(61) = 1.51, p = 0.077, thus refuting H2. It is suspectedthat with more participants, the total average runtime comparison will be significant. Averagewalking velocity for the Mobile condition was 1.13±0.407m

s, 1.03±0.311m

sfor Mobile females, and

1.03 ± 0.311ms

for Mobile males 1.03 ± 0.311ms

. A t-test comparison of average velocity betweenMobile males and females was not significant.

B. Target Selection with Panning

The panning descriptive statistics are provided in Table II. “From Pan” represents the distancetraveled from a pan’s completion to the next target. “From Target” represents the distance traveledfrom target-to-target selections (i.e., target selections that did not require a pan to be performedin between their selection). The Brown-Forsythe Levene-type test compared the effective widths

Hayes et al.: HMT-11-01 10

Search Time Mean(sec) Std. Dev. Count

Seated 2.94 2.34 111Mobile 3.18 2.29 119

Pans per Search Mean(counts) Std. Dev Count

Seated 2.73 4.23 111Mobile 3.05 3.96 119

Search Distance Mean (mm) Std. Dev Count

Seated 261.56 261.32 111Mobile 293.66 322.78 83

TABLE III: Search time, pan count, and distance descriptive statistics.

1 2 3 4 5 6

A

B

C

D

(a) Seated1 2 3 4 5 6

A

B

C

D

(b) Mobile

Target Center

061218243036

Hit

Cou

nts

Offset

Fig. 3: Target hit points by screen quadrant for each condition.

between targets in the “From Pan” and “From Target” cases for each condition. Likewise, Student’st-test was used to compare the hit accuracy between these cases for each condition. None of thesetests yielded significant results.

Search time, pan count, and search distance descriptive statistics are shown in Table III. Notethat once again, descriptive statistics for the Seated and Mobile condition are quite similar. TheMobile condition Search Distance metric contains a count of 83 (versus 119), due to the data metricbeing incorrectly logged at the outset of the evaluation. Student’s t-test comparisons of the searchtime, pan count, and search distance metrics revealed no significance results.

C. Touch Point Skewness

The 24 quadrants were divided into six different regions. The regions are as follows: Outside,the 16 regions that form the outer boundary of the grid; Inside, the 8 internal quadrants of thegrid; Left, the 12 quadrants that form the left half of the grid; Right, the 12 quadrants that formthe right half of the grid; Top, the 12 quadrants that form the upper half of the grid; and Bottom,the 12 quadrants that form the lower half of the grid. The descriptive statistics for each of the sixregions by condition are presented in Tables IV and V.

The distribution of hit points per quadrant is provided for both conditions in Figure 3. Offsetsin Figure 3 are exaggerated so that existing trends in target offset, if any, can be viewed. A meanoffset represented on the edge of a region is equivalent to being 10 screen pixels from a target’scentral axis. No hits occurred in the upper left quadrant (A6) in the Seated case, thus no offset ispresented.

Hayes et al.: HMT-11-01 11

Outside Inside Left Right Top Bottom

Count 136.00 177.00 159.00 154.00 168.00 145.00Mean 8.50 22.13 13.25 12.83 14.00 12.08

Std. Dev. 6.50 5.30 9.79 8.34 8.44 9.61

TABLE IV: Descriptive statistics for the Seated condition screen regions.

Outside Inside Left Right Top Bottom

Count 161.00 8.00 176.00 161.00 190.00 147.00Mean 10.06 22.00 14.67 13.42 15.83 12.25

Std. Dev. 4.43 6.57 9.09 6.32 8.48 6.66

TABLE V: Descriptive statistics for the Mobile condition screen regions.

Confidence intervals (CI) for the hit counts per region were calculated using the adjusted Waldmethod. A lower bound on each CI greater than 50% indicates significant results with a TypeI error rate of 0.05. Only the lower bounds at a 95% CI are reported. 53.67% of hits occurredin the Top region for the Seated condition, and the difference between hit count for the Top andBottom regions was nearly significant, CI95% = 48.14%. 56.37% of hits occurred in the Top regionfor the Mobile condition, and the difference between hit count for the Top and Bottom regions wassignificant, CI95% = 51.04%.

Since the Center and Outside regions were not equal size groups (8 sections for the Center groupvs. 16 in the Outside), full area hit counts are not used for comparison; therefore, a Student’s t-testwas performed. The comparison between hit point difference between the regions was significantfor both conditions, Seated (t(22) = 5.12,p < 0.001) and Mobile (t(22) = 5.30,p < 0.001).

The offset data does not demonstrate the pattern reported by Henze et al. [21], which supportsH3. Also, no significant differences in our data were found between target offsets per region for theSeated and Mobile conditions.

VI. Discussion

A. General Target Selection

The results obtained for general target selection contain some expected and some surprisingresults. H1 was supported; however, it was not expected that a significant difference exists betweenmales and females in the Mobile condition, which may be due to several factors. First, the averageindex finger tip size of a female is smaller than that of a male [25], which may lead to higher accuracyduring target selection. However, if physiology alone contributed to this result, then females willalso have significantly less target selection error in the Seated case. Second, even though significancewas not shown, females did, on average, walk more slowly than males. A slower walking pace mayhave resulted in higher target selection accuracy, a finding that was shown by Bergstrom-Lehtovirtaet al.’s controlled walking study using a treadmill [6].

The effective widths, shown in Table I, to the authors’ knowledge, represent the first effectivewidth calculations for slate-based mobile interaction using a finger. The determined effective widthvalues in all cases are greater than the 6.4 mm value recommended by Lin et al. [17] when usinga stylus for mobile interaction. This result is not surprising since participants in Lin et al.’s studyused a stylus and walked at a slower average speed (0.625m

s[17] versus 1.13m

s).

The effective widths and target selection error results tend to confirm the assertion made by

Hayes et al.: HMT-11-01 12

2

4

6

8

10

12

14

0 4 8 12 16 20 24

Tar

get

Wid

th(m

m)

Acceptable Error (%)

Seated (Data)

Mobile (Model)

Seated (Model)

Mobile (Data)

We

Fig. 4: Target width as a function of error rate.

Bergstrom-Lehtovirta et al.: any amount of walking, even when performed at a pace set by theparticipant, results in increased selection error [6].

No significance was found when comparing Seated and Mobile task completion times, refutingH2. Previous research has found that task completion times between seated and mobile interactiondiffer significantly [17]. Therefore, even though no significance was found, it is expected that withmore participants a significant difference will be obtained for task completion times between seatedand mobile interaction.

Lin et al. obtained a very good fit (R2 = 0.96 for the free-walking scenario) when using Fitts’law. The poor Fitt’s law fit to our data may be due to the real-world nature of the scenario. Eventhough Lin et al. used a free-movement scenario, participants were instructed to follow a tape linethat formed the path of the course [17]. We believe that using a tape line introduces the a similarproblem encountered by Kjeldskov and Stage when using a leader [7]. The taped line allows theparticipant to know his/her intended path without searching ahead. Effective wayfinding can beaccomplished by looking at the floor a few feet ahead of the participant.

It is unknown exactly how participants navigated in Lin et al.’s study; however, the tape line mayhave simplified the attention-intensive, free-movement task more than the experimenters realized,and may have impacted the results. Our study mitigated oversimplification by keeping the pathunknown to the participant. Through issuing turn commands verbally to participants, the partici-pants did not use artificial visual guides (e.g., a leader, tape lines, etc) to assist them while walking.It is believed that the free-walking scenario in our study required a level of attention that, in turn,increased the difficulty of the target selection task. This increased difficulty perhaps contributed tothe Mobile data having a poor fit to Fitts’ law.

It could be argued that issuing verbal commands to the participant over-simplified the taskwhen compared to a real-world scenario. However, in our evaluation participants had to maintain

Hayes et al.: HMT-11-01 13

awareness of their forward motion in order to avoid other individuals, furniture, etc., which requireddiverting attention from the slate’s screen.

While increased difficulty may explain the poor correlation to Fitts’ law in the Mobile condition,it does not explain the poor correlation in the Seated condition. Since the fit of our data to Fitts’ lawis poor in both cases, it indicates that perhaps the issue is something common to both conditions.Touch-based interaction itself may be to blame. Previous studies using finger and/or thumb-basedinteraction have also had poor fits to Fitts’ law [22, 26]. Perhaps the fact that a stylus can moreprecisely select a single pixel than a finger or the thumb allows stylus-based interaction to be moreadequately described by Fitt’s law, which was initially developed from experimental data using anapparatus similar to a stylus [27].

Based on the obtained target error data, recommendations for the appropriate target width toobtain a desired error rate when using finger-based interaction on a mobile slate device can begiven. Figure 4 models the required target sizes to achieve a desired error rate. The models shownare based on the variance of all the hit points. Since Equation 2 results in an We for both the xand y dimension, each We was used to perform a regression to fit the model to the data. For theMobile condition, R2

max = 0.998, and R2min = 0.994. For the Seated condition R2

max = 1.000 andR2

min = 0.999. The larger R2 for each condition was used, which results in an improved model,particularly as the acceptable error percentage decreases. Therefore, despite the fact that Murataused the minimum We [11], the maximum We resulted in a better fit of the model to our data.Murata’s use of the minimum We was based on empirical observation from previous studies.

To the authors’ knowledge, effective width-based models have never been used to recommendtarget sizes based on acceptable error for touch-based interaction. Obtained data suggests thatapplying the two-dimensional effective width model yields a highly accurate model for touch-basedselection error.

B. Target Selection with Panning

The lack of significance in pan time and distance, as well as the search related metrics, indicatesthat panning may not have a large effect on target selection tasks. Despite the lack of significance,the target selection task within a scrolling interface is representative of many common mobilecomputing tasks. The presented panning-related results indicate that perhaps the act of panningdoes not influence the size of interface elements. For example, widgets in a scrolling interface donot have to be larger if they are displayed on-screen as the direct result of scrolling.

Even though no significant result was found, we believe that panning appears to have a directresult on the outcome of the target selection task. Unlike previous work by Lin et al., which found agood fit to Fitts’ law for seated target selection tasks [17], our data did not fit the Fitts’ law model.This lack of agreement with the Fitts’ law model may indicate that more factors are involved thatcause Fitts’ law to no longer apply. The most likely suspect is panning in the Seated case. However,before any definite statements concerning the effect of panning can be made, more data is necessary.

C. Touch Point Skewing

The region based comparisons confirm that, for slate based interaction, hit count is significantlyhigher near the center of the screen than the periphery. Hit counts obtained from thumb-basedinteraction indicate higher hit counts near the corner of the screen where the thumb is anchored[21]. Therefore, significantly higher hit counts in the center region of the slate supports H3. Thehit count result is starkly different from the result obtained by Henze et al. [21], lending supportto Henze et al.’s hypothesis that most users of their target selection application interacted via their

Hayes et al.: HMT-11-01 14

thumbs.The difference in hit point distribution between thumb-based and finger-based touch interaction

indicates that mobile software should be designed to accommodate a variety of different inputs.This is especially true for slate devices, since a user may interact with the device through cradlingit or holding it with one hand and using a finger, or holding it with two hands and using boththumbs. In the first case, based on our results, users interact with the center portion of the screenversus the periphery. In the dual-thumb interaction case, based on Henze et al.’s single-thumbinteraction results [21], users prefer to interact with the regions of the screen that are nearest tothe anchor points of the thumb. Since a user can shift between these two methods of interaction atany time, both must be considered during the user interface design process for slate-based devices.

Henze et al. also reported a trend in target selection offset vectors to point toward the anchorpoint of the thumb [21]. This trend was not observed in our results. While target selection offsetsper grid section appear somewhat similar between the Seated and Mobile conditions (e.g., quadrantsA4, A5, and C6 in Figure 3), there appears to be no overarching offset trend within each condition.Since the finger can move freely in space in order to interact with the touch screen, targets can beapproached from any direction. The base of the thumb is inherently anchored to a position on themobile device, thus targets cannot be approached from an arbitrary direction during thumb-basedinteraction.

A significant difference was found between hit point count in the Top and Bottom regions of thescreen in the Mobile case. Since participants cradled the slate device while walking in the Mobilecondition, this preference for interacting with the top half of the screen may be due to the tilt of thescreen in relation to the participant. While walking, in many cases, the participant tilted the screenaway from his/her body, placing the top of the screen farther away from the participant’s body thanthe bottom. This tilt may have provided an easier angle of approach for the participant’s finger.The tilt of the screen may have also made the top half of the screen easier to see, thus promotingincreased interaction in that portion of the screen.

VII. Conclusions

This work explored target selection error for slate form factor mobile devices. A target selectiontask was implemented within a scrolling map-based interface and participants performed the taskwhile seated and while walking in a free-movement scenario. It was determined that significantlyhigher target selection error exists when performing a target selection task while mobile. Gender alsoplays a role in target selection error for a mobile user. When mobile, female users have significantlylower target selection error than males. The effective target width for the mobile female user is alsosignificantly smaller than the mobile male user. Interestingly, gender has no significant effect ontarget selection error or effective width if a user is seated. The effective width calculations can beused to determine efficient, yet adequate, dimensions for buttons and other interface widgets.

This work also explored the effects of panning on the target selection task. It was believed thatpanning increases the difficulty of the target selection task, resulting in higher effective widths fortargets that are selected directly after panning. However, panning appears to have no significanteffect on target selection for neither seated nor mobile users. There also appears to be no significantdifference in the average pan time or pan distance in either condition.

A model of recommended target width as a function of error rate percentage was also presented.This model was derived based on experimental results and provides a point of reference for designersof mobile applications.

Finally, target offset and hit counts per quadrant were discussed. It was shown that, for mobile

Hayes et al.: HMT-11-01 15

devices that utilize finger-based interaction, the distribution of hit points differs from thumb-basedinteraction. There also appears to be no overall trend for target selection offset per screen region.

VIII. Future Work

Future work will further investigate panning as it pertains to the target selection task. Theauthors feel that it is important to understand panning’s relationship to target selection as the twoacts in combination (scrolling and selection) are commonly used in mobile software applications. Itis believed that developing a clearer indication of the impact of panning upon target selection willhave direct benefits to mobile interface design techniques and best practices standards.

IX. Acknowledgments

This research was partially supported by a contract from the US Marine Corps Systems Commandto M2 Technologies, Inc., National Science Foundation Grant IIS-0643100, and by a Departmentof Defense National Defense Science and Engineering Graduate Fellowship. Additional partialsupport for underlying system development has been provided by the Office of Naval ResearchMultidisciplinary University Research Initiative Program award N000140710749.

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