Dwell Time Reduction Technique using Fitts’ Law
for Gaze-Based Target Acquisition
Toshiya Isomoto

Toshiyuki Ando

University of Tsukuba
isomoto@iplab.cs.tsukuba.ac.jp

University of Tsukuba
ando@iplab.cs.tsukuba.ac.jp

Buntarou Shizuki

Shin Takahashi

University of Tsukuba
shizuki@cs.tskuba.ac.jp

University of Tsukuba
shin@cs.tsukuba.ac.jp

ABSTRACT
We present a dwell time reduction technique for gaze-based target
acquisition. We adopt Fitts’ Law to achieve the dwell time reduction. Our technique uses both the eye movement time for target
acquisition estimated using Fitts’ Law (𝑇𝑒 ) and the actual eye movement time (𝑇𝑎 ) for target acquisition; a target is acquired when the
difference between 𝑇𝑒 and 𝑇𝑎 is small. First, we investigated therelation between the eye movement for target acquisition and Fitts’
Law; the result indicated a correlation of 0.90 after error correction. Then we designed and implemented our technique. Finally,
we conducted a user study to investigate the performance of ourtechnique; an average dwell time of 86.7 ms was achieved, with a
10.0% Midas-touch rate.

CCS CONCEPTS• Human-centered computing → Interaction techniques; Pointing;pointing and can estimate the time required for target acquisition.
In our technique, a target is acquired when the difference between
the eye movement time estimated by Fitts’ Law and the actually
measured eye movement time is smaller than a threshold value.
In this work, we first reinvestigated a relation between the eye
movement for a target acquisition and Fitts’ Law. The result showedthat the correlation (𝑟 2 ) between the eye movement time estimatedby Fitts’ Law and the measured eye movement time was 0.90 after
error correction. We designed and implemented our technique according to this result. We conducted a user study to investigate theperformance of our technique. The result showed that our techniquereduced the average dwell time to 86.7 ms with a 10.0% Midas-touchrate.
The contributions of this paper are as follows: 1) the principle ofdwell time reduction technique, 2) experimental results showingthat the eye movement for target acquisition conforms to Fitts’ Law,
3) implementation method of our technique, and 4) experimental
results showing the performance of our technique.

KEYWORDS
Gaze interaction, eye tracking, dwell-based interaction, eye movement, ISO 9241, Midas-touch
ACM Reference Format:
Toshiya Isomoto, Toshiyuki Ando, Buntarou Shizuki, and Shin Takahashi.
2018. Dwell Time Reduction Technique using Fitts’ Law for Gaze-Based
Target Acquisition. In ETRA ’18: 2018 Symposium on Eye Tracking Researchand Applications, June 14–17, 2018, Warsaw, Poland. ACM, New York, NY,
USA, 7 pages. https://doi.org/10.1145/3204493.3204532

1

INTRODUCTION

Because the dwell time heavily limits the performance of dwellbased target acquisition technique, previous work attempted tominimize the dwell time and the number of Midas-touches [Jacob
1991] by adjusting the button size [Penkar et al. 2012], user andbutton type [Nayyar et al. 2017], and by using next-letter prediction
for typing [Mott et al. 2017]. In this paper, we show another dwell
time reduction technique for gaze-based target acquisition. Our
technique uses Fitts’ Law [Fitts 1954], which models the act of
ETRA ’18, June 14–17, 2018, Warsaw, Poland© 2018 Association for Computing Machinery.
This is the author’s version of the work. It is posted here for your personal use. Not
for redistribution. The definitive Version of Record was published in ETRA ’18: 2018
Symposium on Eye Tracking Research and Applications, June 14–17, 2018, Warsaw,
Poland, https://doi.org/10.1145/3204493.3204532.

2

RELATED WORK

Many gaze-based target acquisition techniques have been explored.
Our technique is based on eye movement for target acquisition.
Thus, we discuss previous work on the eye movement for target
acquisition.

2.1

Gaze-Based Target Acquisition Techniques[Jacob 1991] showed dwell-based target acquisition technique anddefined the notion of Midas-touch in the context of gaze-basedtarget acquisition. [Sibert and Jacob 2000] showed that the dwellbased technique is faster than that using a mouse. However, as
previous work has reported, there is a trade-off between Midastouches and constant dwell time. For example, in a pilot study by[Nayyar et al. 2017] of 192 target acquisitions, a 200 ms dwell timecaused more than 50 Midas-touches and that of 300 ms caused morethan 40 Midas-touches. Based on this result, the authors used 400
ms as the constant dwell time in their user study.
Because constant dwell time heavily limits the performanceof dwell-based target acquisition, previous work has focused on
reducing the dwell time by adjustment. [Penkar et al. 2012] adjusted
it to button size. [Nayyar et al. 2017] adjusted it to each user andbutton type. [Mott et al. 2017] dynamically adjusted it by next-letter
prediction for typing.

ETRA ’18, June 14–17, 2018, Warsaw, Poland

Toshiya Isomoto, Toshiyuki Ando, Buntarou Shizuki, and Shin Takahashi

As an approach to avoid Midas-touches, many gaze-based target acquisition techniques use the hand for click instead of dwell:[Liebling and Dumais 2014; Zhai et al. 1999] used a mouse; [Pfeuffer and Gellersen 2016; Stellmach and Dachselt 2012; Turner et al.
2015] used touch; and [Chatterjee et al. 2015] used hand gestures in
mid-air for target acquisition. Smooth pursuit is another approach to
avoid Midas-touches, in which the user acquires a target using only
the eye by pursuing the moving target displayed near the target[Esteves et al. 2015; Schenk et al. 2017].
Dwell-free target acquisition techniques for text entry may provide faster target acquisition than dwell-based ones. Filteryedping[Pedrosa et al. 2015] is a dwell-free typing technique that predicts
a word from a sequence of characters which the user glances at.
EyeSwipe [Kurauchi et al. 2016] is also a dwell-free typing technique that uses “target reverse crossing” [Feng et al. 2014]; the user
only has to gaze at the first and last characters of the word using“target reverse crossing”, and to move the user’s eye for the othercharacters. From the eye movement, EyeSwipe predicts the word.
By contrast, we focus on designing a general-purpose gaze-basedtarget acquisition technique that allows the user to acquire a target
quickly using only the eyes by dwell time reduction.

2.2

Analyses of Eye Movement in Gaze-Based
Interaction

Eye movement for target acquisition has been explored extensively.
[Zhang and MacKenzie 2007] evaluated the performance of dwellbased target acquisition using a multi-directional pointing task;
they showed that the dwell-based one is faster than that using amouse, although its throughput was lower.
Moreover, some studies have explored the relation between
gaze-based target acquisition technique and Fitts’ Law. [Ware andMikaelian 1987] stated that vertical and horizontal target acquisition using eye movements conform to Fitts’ Law based on theirexperimental results. [Miniotas 2000] derived an 𝑟 2 between the
movement time and Index of Difficulty of Fitts’ Law. In this experiment, a comparison of horizontal target acquisition using amouse and the gaze showed that 𝑟 2 was 0.98 for both conditions.
[Vertegaal 2008] evaluated the performance of four target acquisition techniques (mouse, stylus, gaze-based with a manual click, anddwell-based one) in horizontal target acquisition; 𝑟 2 s were reported:
0.99 for the mouse, 0.98 for the stylus, 0.88 for gaze-based with amanual click, and 0.89 for dwell-based one. [Murata et al. 2015]
evaluated the relation between gaze-based interactions with various target shapes and eight directions, and Fitts’ Law; they founda generalized Fitts’ Law model which well fits their experimental
data with 𝑟 2 = 0.8776–0.9965.
In our work, we use a multi-directional pointing task to investigate the relation between eye movement for target acquisition andFitts’ Law for designing and implementing our dwell time reduction
technique.

3

PRINCIPLE

Our dwell time reduction technique for gaze-based target acquisition is based on Fitts’ Law, as given by Equation 1 [MacKenzie
1989], to recognize whether the eye movement is for the target

acquisition or not:𝑇 = 𝑎 + 𝑏 log2 (𝐷/𝑊 + 1),(1)

where 𝑇 is the movement time for moving the pointer to the target;
𝑊 is the width of the target; 𝐷 is the distance to the target; and 𝑎
and 𝑏 are coefficients that depend on the user and the device. The
term log2 (𝐷/𝑊 + 1) is referred to as Index of Difficulty (𝐼 𝐷), whichshows the difficulty of target acquisition.
Assume that an eye movement conforms to Fitts’ Law. Under
this assumption, if the user moves the gaze from point (𝑃) to an
on-screen icon to acquire it, the eye movement time, width of theicon, and distance from 𝑃 to the icon satisfy Equation 1. Thus, we
can determine whether an eye movement is for target acquisition
or not by testing weather these three variables satisfy Equation 1,
when the gaze enters the icon.
The above test can be implemented using threshold-based approach (Figure 1). First, a calibration is performed to obtain 𝑎 and𝑏 in Equation 1. At runtime, when the eye movement slows andthe gaze enters the icon, we estimate the movement time (𝑇𝑒 ) using
Equation 1. If the difference between 𝑇𝑒 and the actual eye movement time (𝑇𝑎 ) is smaller than a threshold value (𝜏), we regard theicon as the target icon; otherwise, we regard the icon as a non-target
icon (Figure 1a). On the other hand, when the gaze enters the icon
while the eye movement is speeding up or is fast, we regard the icon
as a non-target icon, even if the difference is smaller than 𝜏 (Figure 1b). Thus, the Midas-touch problem is remedied by examining
both the speed of the eye movement and the difference between 𝑇𝑒
and 𝑇𝑎 . However, in the case of short distance between non-target
and target icons, the gaze may slowly enter the non-target icon; if
the difference between 𝑇𝑎 and 𝑇𝑒 is small, Midas-touch may occur(Figure 1c).
In contrast to previous dwell-based target acquisition techniques,
our technique reduce the dwell time because the target icon is
acquired immediately when the gaze enters the icon.

4

EXPERIMENT 1

To implement our technique, we conducted an experiment (Experiment 1) to verify whether the eye movement for target acquisition
conforms to Fitts’ Law.

4.1

Methods

16 participants (all male) were recruited for Experiment 1. Theirage ranged from 21 to 24 years (𝑀 = 22.0). The participants had
normal or corrected (glasses or contact lenses) vision with no color
vision abnormalities; five wore glasses and four wore contact lenses.
None had ever used an eye tracker.
We used Tobii EyeX [Tobii AB 2017] in the experiment, which
was attached to the bottom of a 24-inch non-glare type display,
to prevent reflection; with the resolution of the display was 1,920
× 1,080 pixels. The participant’s head was positioned 60 cm awayfrom the display, which was attached to a height-adjustable arm toallow adjustment of the positional relation between the participant
and the display. The display was placed in front of a white wall
to prevent interference from objects unrelated to the experiment.
Experiment 1 was conducted in a room lit by a LED fluorescentlamp to ensure consistent lighting condition.

Dwell Time Reduction Technique using Fitts’ Law
for Gaze-Based Target Acquisition
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ETRA ’18, June 14–17, 2018, Warsaw, Poland
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Figure 1: Principle of our technique. The gray line represents the eye movement trajectory. The orange region represents the threshold range (𝑇𝑒 ± 𝜏). The Red circle represent
target icon. Blue circles represent a non-target icons. a) Target icon is acquired. b) No
target is acquired. c) Non-target is acquired (Midas-touch).
A multi-directional pointing task (Figure 2) introduced in ISO
9241-411 [ISO 2012] was used. We used five conditions in eachfor target distance (𝐷: 2.00, 4.00, 6.00, 8.00, 10.0 inches) and target
width (𝑊 : 1.00, 1.25, 1.50, 1.75, 2.00 inches).
The participants were required to look at targets on the screenin each trial. Each task comprised 13 trials. Within a session, theparticipant performed 25 tasks (5 distance conditions × 5 target
width conditions) and five sessions were conducted. In total, we
collected data for 26,000 trials (16 participants × 13 trials × 25 tasks
× 5 sessions).
Once the participant’s gaze had entered the target, the eye movement time was recorded. The target then converted to the background color, and the next target was displayed immediately. The
order of the combination of 𝑊 and 𝐷 was randomized for eachsession.
The participants were asked to look consciously at the center of
the target as quickly as possible. The participants were also asked
not to move their head as much as possible, given that the performance of the eye tracker is less robust with head movement.
Prior to the session, the eye tracker was calibrated. The calibration
was performed as many times as necessary by adjusting the positional relation between the participant and the eye tracker using aheight-adjustable arm.
Before the experiment, questionnaires were used to obtain the
demographic information of the participants and their fatigue status; fatigue was evaluated by a 5-point Likert scale. The task began
when the participant pushed the ‘Enter’ key on the keyboard placedby participant’s hand. After looking at all 13 targets, the participants were asked to take a rest if desired to reduce the effect ofthe participant’s eye fatigue. The participants were also asked tocomplete a fatigue questionnaire, and took a rest at least five minutes. The next task began by pushing the ‘Enter’ key. Experiment 1
took approximately 55 minutes per participant. Each participant
received 820 JPY (approximately eight USD).

4.2

0

Results and Analysis

A Wilcoxon signed-rank test showed that there was no significant
difference between fatigue before the experiment and after eachsession (𝑝 > 0.05). No participants stated that they were fatigued

5
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12
1

Figure 2: Multi-directional pointing task. The red circle is the target and the number shows the order of targets.

after the experiment. However, the results indicated apparent misrecognition due to eye tracking problems. Therefore, before testingwhether the eye movement for target acquisition conformed to Fitts’Law, we first examined the results to correct for misrecognition.
4.2.1 Error Correction. We found that the movement time insome trials was unrealistically long, which we call misrecognized
trials. Figure 3 shows the relations between time and gaze–target
distance of a misrecognized trial (red line) and a successful trial (blueline) of a participant. In this misrecognized trial, the participant
looked at the target once; however, the gaze was recognized to falloutside of the target (i.e., misrecognition). Therefore, the participant
then looked at the target again, and then the gaze was recognized to
be within the target. In fact, the participant commented, “I felt thatmy gaze was not recognized appropriately. When this occurred, I
first looked at different area apart from the target, and then lookedat the target again. This time the gaze was recognized”.
We corrected this misrecognition by referenced to the following two observations. (1) The gaze–target distance was shortenedmarkedly, and the distance reduction rate decreased, in both themisrecognized and successful trials (green region in Figure 3). (2)The gaze–target distance was stable for a while after the distance
reduction rate decreased, in misrecognized trials (yellow regionin Figure 3); we assumed that this period was a fixation on thetarget. Based on these observations, we took the time from the
beginning of an eye movement to the beginning of the fixation as
the movement time. The period of fixation was defined at 100 ms
in this error correction.
4.2.2 Correlation. The overall 𝑟 2 between 𝑇𝑒 and 𝑇𝑎 was 0.68
for the original data and 0.90 for the corrected data. There was asignificant difference between the original and corrected data witha Mann-Whitney test (𝑝 < 0.05).
We performed a Smirnov-Grubbs test with 95% confidence interval (𝐶𝐼 ) to assess the difference between 𝑇 which was estimatedusing 𝑎 and 𝑏 in the corrected data, and 𝑇𝑎 in each 𝐼 𝐷. As theresult, three combinations of 𝑊 and 𝐷 were detected as outliers:
𝑊 ×𝐷 = 2.00 × 2.00 inches, 2.00 × 4.00 inches, and 1.75 × 4.00 inches;
these results are consistent with the findings of Drewes ([Drewes
2010a], p. 68). Therefore, we derived 𝑟 2 excluded four conditions

Toshiya Isomoto, Toshiyuki Ando, Buntarou Shizuki, and Shin Takahashi
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ETRA ’18, June 14–17, 2018, Warsaw, Poland

800
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Time [ms]Figure 3: Relation between time and gaze–target distance.
The red line shows a misrecognized trial. The blue line
shows a successful trial. The green region shows the period
during which the gaze approaches the target. The yellow region is the fixation period.

(𝑊 = 2.00 and 1.75 inches and 𝐷 = 2.00 and 4.00 inches) to derive
an 𝑟 2 of 0.95.

5IMPLEMENTATION

To obtain 𝑇𝑒 and 𝑇𝑎 , we use an eye movement detection system. A
calibration system is used to derive the constants of Equation 1.

5.1

Eye Movement Detection

Eye movement detection is used to acquire the target regardless ofthe original gaze point estimated by the eye tracker. The distanceto the target, 𝐷, used to calculate 𝑇𝑒 is calculated starting from thepoint at which the gaze first moved towards the target center. 𝑇𝑎
corresponds to time between the gaze beginning to move and thegaze entered the target. We use the eye movement distance fromthe gaze point of one sample before for the eye movement detection.
We define four eye movement states: fixation, start of movement,
movement, and end of movement. These states are defined with thesame mechanism discussed in Section 4.2.1.
First, the fixation state corresponds to when the eye movement
distance is shorter than short distance (𝑑𝑠 ). The eye movement stateis start of movement when the eye movement distance exceeds 𝑑𝑠
during fixation; additionally, fixation can occur again if the eye
movement distance is shorter than 𝑑𝑠 during start of movement.
Then, the eye movement state is movement when the eye movement
distance exceeds long distance (𝑑𝑙 ) during start of movement. Finally,
the eye movement state is end of movement when the eye movement
distance is shorter than 𝑑𝑙 during movement. The eye movement
state is fixation when the eye movement distance is shorter than 𝑑𝑠
during end of movement. During end of movement or fixation afterend of movement, a comparison of 𝑇𝑒 and 𝑇𝑎 is performed to test
whether the eye movement is for target acquisition or not.
With this implementation, if the gaze passes a non-target icon
in movement, the comparison is not performed (same as the icon in
Figure 1b) and thus, Midas-touches are avoided. However, if the gazepasses a non-target icon in end of movement or fixation after end of
movement, the comparison is performed and thus Midas-touches
potentially occur.

Figure 4: Display used in Experiment 2. The red circle is atarget. The blue circles are non-targets. The green circle isan acquired circle.

5.2

Calibration for Deriving Constants

Our technique requires that the constants of Equation 1 be derived
for each user and each device beforehand. To do this, we adopt
the same multi-directional pointing task used in Experiment 1 forthe calibration. Currently, this calibration is performed using three
conditions for 𝑊 (1.00, 1.25, and 1.50 inches) and 𝐷 (6.00, 8.00, and10.0 inches); note that these conditions remained after the outliers
which is detected in Section 4.2.2 were removed in Experiment 1.

6

EXPERIMENT 2

We conducted a user study (Experiment 2) to investigate the dwell
time and Midas-touch rate of our technique, which were then compared to the previous dwell-based target acquisition technique(hereinafter, previous technique). We assumed a 400 ms dwell time
for the previous technique, which is the dwell time used by OptiDwell [Nayyar et al. 2017].

6.1

Methods

17 participants (P1–P17; all male) were recruited for Experiment
2. Their age ranged from 20 to 24 years (𝑀 = 22.0). Eight (P1–P8)
did not wear glasses or contact lenses; seven (P9–P14, P17) wore
glasses; and two (P15, P16) wore contact lenses. One participant(P16) had abnormal color vision and eight (P1–P5, P10, P11, andP15) had also participated in Experiment 1; thus, these participants
had previous experience with eye tracker and the others did not.
The same eye tracker and display used in Experiment 1 wereused in Experiment 2. In Experiment 1, we were unable to stabilize
the performance of Tobii EyeX due to head movement of the participants. Moreover, the participant commented, “It was difficult toprevent head movement”. Thus, we used a chin rest in Experiment
2 to minimize head movement. The other experimental conditions,such as lighting condition, were the same as Experiment 1.
In Experiment 2, for each trial, participants were asked to acquire
one target from 40 circles were arranged in a 5 × 8 gird (Figure 4).
Each task included 40 target acquisition (i.e., 40 trials). The target
circles were presented in a random order. Three target width wereused to investigate the effect of target width on the performanceof our technique. In each session, the participants performed threetasks, one for each target width (1.00, 1.25, and 1.50 inches). To
reduce order effects, we divided the participants into two groups ofeight participants. One group began with our technique; anotherbegan with the previous technique. The participants performed
two sessions with each technique. In total, 3,840 trials (40 trials × 3

Dwell Time Reduction Technique using Fitts’ Law
for Gaze-Based Target Acquisition

ETRA ’18, June 14–17, 2018, Warsaw, Poland

Answerd Rate [%]80

0Figure 5: Questionnaire result regarding target locations
that are difficult to acquire. Deeper color shows more difficult target locations.

tasks × 2 sessions × 16 participants) of data were collected for eachtechnique.
Before the experiment, we used the results of Experiment 1 todetermine 𝜏 using the following equation:
𝜏 = 0.031 + 0.024 log2 (𝐷/𝑊 + 1).

(2)

The constants in Equation 2 were based on 𝑆𝐷 of the error-corrected
result (𝑎 = 0.031, 𝑏 = 0.024). 𝐷 was the calculated by using themechanism discussed in Section 5.1.
We asked the participants to look at the target as quickly andconsciously as possible, and to keep looking at the target until thetarget was acquired. When a circle was acquired (even when it
was a non-target circle), it turned green. If the target was correctly
acquired, or if it was not acquired after more than five seconds, thetarget turned green and then the next target appeared. In Experiment 2, two calibrations were performed: one for Tobii EyeX and
one for our technique. The participant performed the calibration of
Tobii EyeX before each task; this calibration was done more thanonce and in the same way as for Experiment 1. Another calibration, i.e., that for our technique, was conducted before each session:
when 𝑟 2 was higher than 0.90 (i.e., the error-corrected result), we
used 𝑎 and 𝑏 derived from the calibration; in all other cases, theparticipant repeated the calibration of our technique.
The participants were asked to rest more than one minute between tasks to reduce eye fatigue; more than one minute of rest wasalso taken after the calibration of our technique. After a session, theparticipant was asked to complete a questionnaire regarding theirimpression, target locations that were difficult to acquire, and eye
fatigue using a five-point Likert scale. Then, we asked the participants to remove their head from the chin rest to take a rest of morethan five minutes. After all sessions, the participant were asked
to complete a questionnaire that asked, “Which technique did you
feel fatigue with?” with reasons in a free-form text. Experiment 2
took approximately 83 minutes per participant. Each participant
received 1,640 JPY (approximately 15 USD).

6.2

Results

We performed a Wilcoxon signed-rank test (𝛼 = 0.05) on the fatigue
scores: there was a significant difference between both techniquesin the first session and no significant difference between them inthe second session.
During the experiment, the gaze estimated by Tobii EyeX for one
participant who wore glasses (P17) was seriously dispersed eventhough calibration of Tobii EyeX was successful. Thus, the resultsfor P17 were excluded and we used those from the remaining 16
participants. We also classified the trials into three categories forthe analyses: successful, Midas-touch, and failed trials. A successful
trial was obtained when the target was acquired with no Midastouches. A Midas-touch trial was obtained when a non-target circle
was acquired and a failed trial was obtained when no circle was
acquired within five seconds. The results showed that there were
6,284, 368, and 1,028 of the successful, Midas-touch, and failed
trials, respectively. Questionnaire results are shown in Figure 5;
the outermost border of the grid (especially the four corners) wereanswered, which is consistent with [Feit et al. 2017]. Moreover, both
P15 and P16 who wore contact lenses commented, “I have dry eye
syndrome and it was difficult to keep my eyes open and look at
the target continuously”. Therefore, we investigated variation in
performance by vision condition (normal or corrected) of the eye,as described in the following sections.
6.2.1 Dwell Time. We calculated the dwell time, of the successful trials of our technique, as the time between the gaze entering
the target and acquisition of the target (Figure 6, left). The dwell
time was 86.7 ms (𝑆𝐷 = 24.2) using our technique and 409 ms1(𝑆𝐷 = 1.38) using the previous technique. Therefore, our techniquereduced the dwell time by 78.8%.
With respect to vision condition of the eye, the dwell time was98.1 ms (𝑆𝐷 = 25.1) under normal vision conditions and 75.3 ms(𝑆𝐷 = 16.6) under corrected vision conditions. There was no significant difference between normal and corrected vision conditions
with a Mann-Whitney test (𝑝 > 0.05).
The left of Figure 7 shows the dwell time by target width conditions using our technique: 78.8 ms (𝑆𝐷 = 23.9) at 1.00 inch, 91.7 ms(𝑆𝐷 = 26.1) at 1.25 inches, and 89.6 ms (𝑆𝐷 = 28.4) at 1.50 inches.
We performed Wilcoxon signed-rank tests (𝛼 = 0.05); the results
showed that there was a significant difference between 1.00 and1.25 inches, and no significant difference between any other pair.
6.2.2 Midas-touch Rate. We calculated the Midas-touch rate ofour technique as the total number of Midas-touch trials divided by
the sum of the number of successful and Midas-touch trials (Figure 6,
right). The rate was 10.0% (𝑆𝐷 = 4.76) using our technique and0.70% (𝑆𝐷 = 0.79) using the previous technique.
With respect to vision condition of the eye, the Midas-touch rate
was 6.10% (𝑆𝐷 = 2.84) under normal vision conditions and 14.0%(𝑆𝐷 = 2.47) under corrected vision conditions. There was a significant difference between normal and corrected vision conditions
with a Mann-Whitney test (𝑝 < 0.05).
The right of Figure 7 shows the Midas-touch rate by target widthconditions in our technique: 7.26% (𝑆𝐷 = 4.29) at 1.00 inch, 8.32%(𝑆𝐷 = 5.95) at 1.25 inches, and 14.3% (𝑆𝐷 = 9.24) at 1.50 inches.
We performed Wilcoxon signed-rank tests (𝛼 = 0.05); the results
showed that there was a significant difference between 1.50 inches
and smaller target width conditions and no significant difference
between 1.00 and 1.25 inches.

1 Even though we used 400 ms as the dwell time in the previous technique, it was
409 ms, since the sampling rate of Tobii EyeX was 60 Hz, which involves at least 16 msof errors.

ETRA ’18, June 14–17, 2018, Warsaw, Poland

Toshiya Isomoto, Toshiyuki Ando, Buntarou Shizuki, and Shin Takahashi

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Figure 6: Performance of our technique. Left: dwell time; right: Midas-touch rate. Error bars indicate ± one SD.
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necessity of calibration of your technique”. In Experiment 2, calibration of our technique used nine combinations (three 𝐷 × three
𝑊 ). To reduce the fatigue associated with the calibration of ourtechnique, we plan to use smaller number of combinations withwidely distributed IDs (e.g., four combinations whose IDs are 1.00,
2.00, 3.00, and 4.00).

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1.00

1.25

1.50

Figure 7: Performance by target width condition. Left: dwelltime; right: Midas-touch rate. Error bars indicate ± one SD.

6.3

Discussions[Nayyar et al. 2017] reported a Midas-touch rate of 26.0% using a
200 ms dwell time; however, we achieved a considerable dwell time
reduction at a low Midas-touch rate, although this comparison is
not appropriate because the conditions of both experiments weredifferent. With respect to target width conditions, the largest circle
was caused the highest Midas-touch rate (Figure 7, right). In the caseof largest circle, the gaze point estimated by the eye tracker tended
to enter circle due to dispersion of the estimated gaze. Additionally,
the distances between the point of start of movement and the circles
neighboring the target were nearly the same as that of the target (i.e.,
𝑇𝑒 s were nearly the same), such that the Midas-touch trials occurred
when the estimated gaze entered the neighboring circles. Given that
the Midas-touch rate was lowest under the largest target condition,and that the smallest target condition caused the highest numberof failed trials (1.00 inches: 18.0%; 1.25 inches: 9.30%; 1.50 inches:
7.19%), 1.25 inches was considered the most suitable target width,
although 1.00 inch could also be used if the performance of the eye
tracker improves.
Some participants (two in Experiment 1 and one in Experiment 2)commented that “I felt that the target was acquired before I lookedat the target”. This suggests that too short dwell time (e.g., 0 ms) is
not suitable for dwell-based target acquisition. We plan to conduct
a user study to establish optimal dwell time. Moreover, as many
trials in which circles were presented towards the outermost borderof the grid were failed, as described above, the middle of the display
is most appropriate with respect to target position.
Regarding the questionnaire of “Which technique did you feelfatigue with?”, four participants answered the previous technique,three answered our technique, and eleven answered no difference.
Two participants in Experiment 2 commented that “I felt that yourtechnique was more tired than the previous technique, due to the7

LIMITATIONS AND FUTURE WORKIn the results of Experiment 1, gaze-based target acquisition highly
conformed to Fitts’ Law. However the participants had similar characteristics (16 healthy young men). Additionally, in Experiment 2,
one participant (P16) had abnormal color vision. P16 commented, “I
have color vision abnormalities. So, it was difficult to move my gazefrom a green circle to a red circle when these circles were neighboring”. Therefore, we need to evaluate 𝑟 2 and the performance of
our technique with more various attributes: women, younger andolder individuals, and people with neurodegenerative diseases (e.g.,amyotrophic lateral sclerosis).
Moreover, some modified Fitts’ Law coping with different conditions were proposed: target shape [Hoffmann and Sheikh 1994; Murata et al. 2015] and pointer movement direction [Murata et al. 2015].
We plan to examine whether these models conform to our technique. Furthermore, we will modify our technique to dynamicallyselect the most appropriate model, depending on the conditions, toimprove the performance.
Although some previous studies have shown that the eye movement conforms to Fitts’ Law [Miniotas 2000; Ware and Mikaelian
1987], which is consistent with our results, [Drewes 2010a,b] reported that the eye movement would not conform to Fitts’ Law.
Moreover, [Carpenter 1988] showed that eye movement conforms
to the formula that is independent of target width. Therefore, it isnecessary to further investigate the relation between eye movement
and Fitts’ Law and other formulas in more detail.

8

CONCLUSION

We presented a dwell time reduction technique for gaze-based target
acquisition based on Fitts’ Law. Our technique used both the eye
movement time for target acquisition estimated using Fitts’ Law
and the actual eye movement time for target acquisition. In our
technique, a target is acquired when the time difference between
the estimated and actual eye movements is small. Based on the
results of the user study for investigating the performance of ourtechnique; an average dwell time of 86.7 ms was achieved, with a
10.0% Midas-touch rate.

Dwell Time Reduction Technique using Fitts’ Law
for Gaze-Based Target Acquisition

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