# EyeTAP: A Novel Technique using Voice Inputs to Address the Midas Touch
Problem for Gaze-based Interactions

Mohsen Parisay  Concordia University1515 Saint-Catherine WestMontrealQuebecH3G
2W1Canada<EMAIL_ADDRESS>,  Charalambos Poullis  Concordia
UniversityMontrealQuebecCanada<EMAIL_ADDRESS>and  Marta Kersten
Concordia University1515 Saint-Catherine WestMontrealQuebecCanada
<EMAIL_ADDRESS>
(2018)

###### Abstract.

One of the main challenges of gaze-based interactions is the ability to
distinguish normal eye function from a deliberate interaction with the
computer system, commonly referred to as ’Midas touch’. In this paper we
propose, EyeTAP (Eye tracking point-and-select by Targeted Acoustic Pulse) a
hands-free interaction method for point-and-select tasks. We evaluated the
prototype in two separate user studies, each containing two experiments with
33 participants and found that EyeTAP is robust even in presence of ambient
noise in the audio input signal with tolerance of up to 70 dB, results in a
faster movement time, and faster task completion time, and has a lower
cognitive workload than voice recognition. In addition, EyeTAP has a lower
error rate than the dwell-time method in a ribbon-shaped experiment. These
characteristics make it applicable for users for whom physical movements are
restricted or not possible due to a disability. Furthermore, EyeTAP has no
specific requirements in terms of user interface design and therefore it can
be easily integrated into existing systems with minimal modifications. EyeTAP
can be regarded as an acceptable alternative to address the Midas touch.

Human-computer interaction, eye tracking, Midas touch, voice recognition,
dwell-time, hands-free interaction, touchless interaction

††conference: Feb; 2020; ††journalyear: 2020††copyright: usgovmixed††doi:
0000001.0000001††ccs: Human-centered computing Pointing devices

##  1\. Introduction

Modern eye tracking sensors offer a suitable alternative to conventional input
devices (i.e. keyboard and mouse) for users for whom manual interaction might
be difficult or impossible. However, gaze-based interaction has well-known
challenges the most important of which are (1) Midas touch where a system
cannot distinguish the basic function of the eye (i.e. looking and perceiving)
from deliberate interaction with the system, and (2) eye jitter which is
caused by small physiological eye movements occurring during a fixation to
perceive a scene visually (Miniotas et al., 2004). In this paper, we propose
EyeTAP (Eye tracking point-and-select by Targeted Acoustic Pulse), an
effective multimodal solution to the Midas touch problem. Specifically, our
method integrates the user’s gaze to control the mouse with audio input
captured using a microphone to trigger button-press events for real-time
interaction.

The contributions of this paper are twofold. Firstly, we have designed and
developed an effective, multimodal interaction technique EyeTAP. The proposed
approach is low-cost and allows for a completely hands-free interaction
solution between the user and the computer system using only an eye-tracker
and an audio input device. Secondly, we present two independent user studies
each with two experiments comparing EyeTAP with all other widely-used
interaction techniques. The analysis of the results clearly shows that using
EyeTAP has at least comparable performance with the mouse. Furthermore, EyeTAP
reaches competitive performance with the remaining eye-based interaction
methods in cases where users would have restricted physical movement, or where
manual interaction with an input device is not possible, e.g. medical
practitioner having both hands busy.

##  2\. Related Work

In eye-based interaction, the Midas touch problem occurs when a user
accidentally activates a computer command by looking when the intention was
simply to look around and perceive the scene. According to Jacob (Jacob,
1990), this problem occurs because eye movements are natural, e.g. the eyes
are used to look around an object or to scan a scene, often without any
intention to activate a command or function. This phenomenon is one of the
major challenges in eye interaction techniques and diverse methods have been
proposed to address the Midas touch problem. The solutions can be categorized
into four groups according to the interaction technique they employ: (a)
dwell-time processing, (b) smooth pursuits, (c) gaze gestures, and (d)
multimodal interaction. Below, we describe each of these solutions and provide
example use-cases.

###  2.1. Dwell-time processing

Dwell-time is the amount of time that the eye gaze must remain on a specific
target in order to trigger an event. Researchers have tried to detect specific
thresholds to handle the Midas touch problem (Pi and Shi, 2017; Velichkovsky
et al., 2014). For example, Pi _et al._ proposed a probabilistic model for
text entry using eye gaze (Pi and Shi, 2017). They reduced the Midas touch
problem by assigning each letter a probability value based on the previously
chosen letter such that a letter with lower probability requires a longer
activation time to be activated and vice-versa. Velichkovsky _et al._ applied
focal fixations to resolve the Midas touch problem by assigning the mean
duration time (empirically set to 325 ms) of a visual search task to trigger a
function (Velichkovsky et al., 2014).

Dwell time has been shown to be even faster than the mouse in certain tasks,
e.g. selecting a letter given an auditory cue (Sibert and Jacob, 2000).
However, with dwell time there is a trade-off between accuracy and speed
(Špakov and Miniotas, 2004; Vertegaal, 2008; Majaranta et al., 2006). The
method of applying focal fixations may be very subjective since searching time
varies across users (Bednarik et al., 2009). Moreover, increasing the
threshold may increase the duration time of the entire interaction.
Conversely, reducing the amount of dwell-time may lead to more errors for some
users (Vertegaal, 2008).

###  2.2. Smooth pursuits

Smooth pursuits are a form of eye movement that occurs when a moving stimulus
(e.g. an object or animation) is followed with gaze (Barnes, 2012). The method
is typically implemented by using two visual points on the interface that
appear on top and below each target. Then to activate the target the user must
fixate on one of these points. This technique has been used to select targets
(Vidal et al., 2013), control home appliances (Velloso et al., 2016), to
activate functions such as mouse clicks (Schenk et al., 2017) or to use the
music player on a smartwatch (Esteves et al., 2015). Schenk _et al._ proposed
a framework (GazeEverywhere) which enables users to replace mouse inputs
(Schenk et al., 2017). This solution includes a computer to process gaze
interactions (gaze PC), a computer to show the results (unmodified PC) which
are connected via a micro-controller to trigger mouse click events, and a
glass pane to project gaze targets on a second screen.

Vidal _et al._ introduced an interaction technique (Pursuits) for large
screens using moving objects to be activated by eye gaze (Vidal et al., 2013).
They used a Tobii X300 eye tracker and a public display to select targets on
the screen. Velloso _et al._ presented a framework (AmbiGaze) to control
ambient devices such as TVs and stereos (each assigned with an infrared (IR)
beacon) with eye gaze using a head-mounted eye tracker (Velloso et al., 2016).
The system employs a server to process gaze inputs and control the devices.
Esteves _et al._ presented a framework for a multi-touch Android smartwatch
(Callisto 300) to input commands using a head-mounted eye tracker (Pupil Pro)
(Esteves et al., 2015). They developed three use-cases: a music player, a
notifications panel with six colored points on the smartwatch screen
representing six applications (e.g. social media apps), and a missed call menu
with four commands, call back, reply text, save number and clear the
notification.

###  2.3. Gaze gestures

Gaze gestures are sequences of eye movements that follow a predefined pattern
in a specific order (Drewes and Schmidt, 2007). Researchers have proposed
techniques which can be applied to analyze eye movements to detect unique
gestures (e.g. (Bâce et al., 2016; Drewes and Schmidt, 2007; Hyrskykari et
al., 2012; Istance et al., 2010)). Drewes _et al._ assigned up, down, left,
right and diagonal directions to different characters on the keyboard thereby
allowing a user to select a letter by moving the eye gaze in any direction
(Drewes and Schmidt, 2007). In addition, they tried to distinguish between
natural and intentional eye movements by using short fixation times during
gesture detection and long fixation times to reset the gesture recognition.
Istance _et al._ developed two-legged and three-legged gaze gestures (up, down
and diagonal patterns) for command selection to play World of Warcraft for
users with motor impairment disabilities (Istance et al., 2010).

In a similar work, Hyrskykari _et al._ studied both dwell-time and gaze
gesture interactions in the context of video games and found that gaze
gestures had better performance for command activation (Hyrskykari et al.,
2012). Moreover, gaze gestures produced fewer errors than the dwell-time and
led to less visual distractions. Bâce _et al._ proposed an AR prototype,
containing a head-mounted eye tracker and a smartwatch, to embed virtual
messages to real-world objects to be shared with peer users (Bâce et al.,
2016). The authors integrated eye gaze gestures as a pattern to encode and
decode messages attached to a specific object previously tagged by another
peer user, thus using gaze gestures as an authentication mechanism for secure
communication.

###  2.4. Multimodal Interaction

Multimodal techniques apply extra inputs from another modality (e.g. touch,
audio, etc.) as the trigger of a function in addition to eye tracking. They
can be divided into the following sub-categories: using mechanical switches,
touch interaction, or facial gestures.

####  2.4.1. Applying a specific (mechanical) switch

For some specific domains, such as rehabilitation, and user groups (i.e. users
with motor impairments or severe disabilities), researchers have applied
specific switches to activate an event or function. For instance, Rajanna _et
al._ proposed a combined framework for users with disabilities which applies a
foot pedal device to click on objects and to enter text (Rajanna and Hammond,
2018). Meena _et al._ applied a soft button on a wheelchair to control the
movements of the wheelchair in different directions (horizontal, vertical and
diagonal) (Meena et al., 2017). Sidorakis _et al._ applied a switch for a
gazed-controlled multimedia framework on virtual reality head-mounted displays
(Oculus Rift) to resolve the Midas touch problem (Sidorakis et al., 2015).
Biswas _et al._ proposed a joystick to control point-and-select tasks for
combat aviation platforms to address the Midas touch problem (Biswas and
Langdon, 2015).

####  2.4.2. Touch interaction

Some researchers have proposed the integration of using touch interaction, for
a limited number of functions, to increase the accuracy of target selection.
Pfeuffer _et al._ applied a cursor at the gaze point to be controlled by a
finger holding a tablet where a finger tap on the screen leads to a click on
the current location of the pointer (CursorShift method) (Pfeuffer and
Gellersen, 2016). In a similar study by Pfeuffer _et al._ , the authors
investigated the integration of finger touch and pen inputs on a tablet for
zooming or annotating tasks on images (Pfeuffer et al., 2016). Although this
technique was not introduced as a solution to the Midas touch problem, it can
increase the accuracy of selection which leads to reducing Midas touch.

####  2.4.3. Facial gestures recognition

In (Rozado et al., 2017), Rozado _et al._ studied the potential of using live
video monitoring to detect facial gestures to enhance eye tracking
interaction. In their work (FaceSwitch), they associated facial gestures
(opening mouth, raising eyebrows, smiling and twitching the nose up and down)
to simulate left and right mouse clicks and customized some keyboard functions
such as page down key press.

Using a multimodal solution that combines eye-gaze with acoustic inputs (audio
or speech detection) can be regarded as an alternative to the reviewed
solutions and has the advantage of not requiring either extra hardware or a
specialized user interface design. For this reason, we designed EyeTAP to use
audio processing for selection. Our solution: (1) provides a hands-free
interaction technique for users with special needs, and (2) addresses the
Midas touch problem. Although there has been some work done on audio detection
to simulate system events for computer interactions (e.g. (Patel and Abowd,
2007; Dey et al., 2004; Hartmann et al., 2007)) the focus has been on signal
processing for complex interactions. Conversely, in our work we applied
acoustic inputs only as a way of sending commands.

##  3\. EyeTAP Prototype

A simple mouse interaction consists of moving the pointer to a target
(pointing phase), and clicking on it to trigger a function (selection phase).
In the EyeTAP prototype the mouse pointer position is captured using the Tobii
4C tracker 111https://tobiigaming.com/product/tobii-eye-tracker-4c/ and
selection is done by generating an acoustic pulse by mouth (e.g. a mouth
click) which is captured by a headset microphone (Logitech H370). The EyeTAP
prototype was developed and the experiments were run on a commodity computer
system: 64-bit Windows 10 PC with Intel i7 2.67GHz CPU, 12 GB RAM, 1 TB hard
disk and NVIDIA GeForce GTX 770 graphics card. Thus, EyeTAP is a cost-
effective system that can be applied at almost any work space. Figure 1
illustrates the EyeTAP system setup.

Figure 1. EyeTAP system: The eye tracker is used to move the pointer from A to
B. The user makes an acoustic pulse by mouth and the signal processing module
interprets the signal as an input and triggers a click event to select B. The
system has an ambient noise tolerance of up to 70 dB.

###  3.1. Eye Tracking: Pointing Phase

The Tobii SDK (TobiiEyeXSdk$-$Cpp$-$1.8.498) supports different events related
to eye tracking activities such as providing the location of the current eye
gaze, positions of both eyes, fixation points and user presence in front of
the eye tracker. We employed the eye gaze library (API) to obtain users’ gaze
locations. These locations show the current gaze position on the screen as
pixels. The SDK supports eye movements in a 3D coordinate system (horizontal,
vertical, depth) but we applied a 2D coordinate system (x,y) such that the
mouse cursor was synchronized with the gaze positions to control the mouse
pointer on the screen. Eye-tracking for the EyeTAP prototype was developed in
C++ and integrated as a new plug-in into the Tobii SDK.

###  3.2. Auditory Processing: Selection Phase

To simulate a click on the item to be selected a headset microphone listens to
the user while suppressing the background ambient sounds/noise (conversations
in office and equipment sounds) in real-time. The intensity of the mouth noise
and distance of microphone is adjusted by the user before the test. A detected
pulse in the real-time audio signal (a value larger than a predefined
threshold) is regarded as a click. The threshold’s value can be adjusted based
on the environment to reduce background ambient noise. The EyeTAP prototype
has an ambient noise tolerance of up to 70 dB. When a significant increase in
the frequency spectrum (greater than the threshold) is detected a mouse click
event is triggered. In general, recording is categorized into two phases:
audible and silent periods. Any audible period with an intensity greater than
the predefined threshold will be detected as an input signal to the system as
the binary 1; similarly, values smaller than the threshold value are regarded
as binary 0. The intuition behind the auditory processing was inspired from
the simplicity of the Morse code (of Encyclopaedia Britannica, 2018), which
consists of a series of ON/OFF signals triggered by tone or light. Information
is interpreted using dots and dashes and therefore can be used to represent
transmitted signals through a sequence of True/False variables.

##  4\. Evaluation

To evaluate the effectiveness of the developed EyeTAP prototype, we ran two
independent user studies each with two internal experiments with 33
participants (13 female, from 22 to 35 years old, SD=2.96). All subjects
partook in both experiments. Prior to running the experiments, subjects were
informed about the purpose of the study, trained on each of the methods to be
tested, and participated in a pre-test questionnaire probing them on their
background in the fields of eye tracking, voice recognition technologies and
their preferred kind of interaction in the case of hands-free alternatives.
The Tobii calibration software was used to calibrate the system for each
participant before starting the study. At the end of the two experiments
subjects filled out a post-test questionnaire, which consisted of the NASA TLX
questionnaire (Group, 1986) followed by specific questions about the subjects’
perceptions of the different interaction methods. The order of interaction
method was randomly selected for each participant. We played an artificial
ambient noise through stereo desktop speakers of 50 dB to simulate a typical
work environment since EyeTAP and voice recognition rely on audio inputs.

###  4.1. User Study 1: Matrix-based Test

In the first experiment, the EyeTAP interaction method was compared with: (a)
the mouse, (b) dwell-time, and (c) eye tracking with voice-recognition. In
this experiment, a matrix of buttons (targets), were randomly distributed
across the screen. The task of the subjects was to point and click on buttons
shown on the screen in increasing numerical order for various levels of
difficulty from 1 (easy) to 5 (hard), described in detail below. The order of
interaction methods seen by each subject was randomly selected for each
participant however, the level of difficultly was presented in ascending
order.

####  4.1.1. Stimulus

The stimulus consisted of 77 buttons (11 columns $\times$ 7 rows) some labeled
with numbers and others not, which covered the entire screen at a resolution
of 1920 $\times$ 1080 pixels on a Dell P2411Hb monitor. Two marginal columns
(far left, far right) and two rows (top, bottom) were removed from the active
selection due to the high difficulty to be selected by users during the pilot-
test. Buttons that were not labeled are considered as barriers or
distractions. To provide feedback to the subject, labeled buttons change color
after the user has successfully pointed and selected on the correct button.
Wrongly selected barriers (buttons with no label) are highlighted in red. The
level of difficulty of the stimulus was also increased across subject trials.
This was done by increasing the number of targets that had to be selected by
the subject. Five levels of difficulty were used for each interaction method:
level 1 (4 targets), level 2 (6 targets), level 3 (8 targets), level 4 (10
targets) and level 5 (12 targets). Targets were randomly distributed over the
entire screen for each level. Figure 2 shows the matrix-based test during
difficulty level 5. The cursor that was used was a black circle because it is
easier for users to keep it on the target’s boundary rather than a pointer.

Figure 2. The matrix-based test for difficulty level 5. Target buttons are
distributed randomly across the screen. The red button illustrates an error.
The black circle on number 12 shows the current eye gaze location. Labels were
enlarged for higher visibility.

####  4.1.2. Mouse

For the mouse method (our baseline method for comparison), subjects simply
used a mouse to move to targets and select them in numerical order.

####  4.1.3. Dwell-time

For the dwell-time method, where an internal timer is used to determine if a
target was selected. The range of dwell-time is in (300-1100) milliseconds for
target selection (Špakov and Miniotas, 2004). Then we defined the target
activation threshold to 500 milliseconds, since it showed best performance in
(MacKenzie, 2012) and participants preferred a dwell-time around 500 ms in a
user study (Špakov and Miniotas, 2004). In other words, a target was selected
when a subject focused on a target for 0.5 seconds, and if the subject moved
their gaze away from the target prior to 0.5 seconds the target selection
process would restart.

####  4.1.4. Eye Tracking with Voice recognition

For voice recognition, eye tracking was used for pointing and voice for
selection. The method was developed using the built-in Windows 10 speech
recognition capabilities available in the .NET framework. We implemented a C#
application to respond to the activation keyword ’select’ to trigger a mouse
click. The same microphone was used as for the EyeTAP test.

####  4.1.5. Measures

The following variables were recorded: completion time, path cost of selecting
targets, error locations, and cognitive load (based on the NASA TLX scores).
An internal logging module recorded subjects’ actions, selection times, as
well as the number of correct and wrong selections. For the path cost measure
the shortest path between targets and the produced path by each interaction
method was processed. To compare the shapes of the generated paths, we used
the dynamic time warping (DTW) algorithm (Bellman and Kalaba, 1959; Myers et
al., 1980; Sakoe and Chiba, 1978). Since DTW works on a time-value domain the
paths produced by the eye tracker were decomposed into their horizontal and
vertical values and compared with their associated shortest path models’ X and
Y values. We applied the built-in DTW function in the Python DTW 1.3.3 module
222https://github.com/pierre-rouanet/dtw to measure the deviations of each
path from the shortest path model.

###  4.2. User Study 1: Dart-based Test

The purpose of this experiment was to measure the accuracy of EyeTAP in
comparison to the previously proposed eye-based interaction methods. The task
of the subject was to select, as accurately as possible, the bull’s-eye of a
dart target using each interaction method. In this experiment, the eye tracker
was used for the pointing phase for each of the interaction methods, however
selection of the target was triggered by different methods, i.e. dwell-time,
voice command or EyeTAP acoustic signal. In order to take into consideration
the fact that eye tracking has different accuracy in different regions of the
monitor, we computed an average value based on five trials for each
interaction method where the stimulus was shown at different areas of the
screen near the center of the screen randomly. Each new randomly chosen trial
began two seconds after selection of the previous target, allowing users time
to change their gaze and to focus on the new target. For the dwell-time
method, a countdown (5 to 0) representing remaining 100 milliseconds was
displayed during the selection phase and users needed to focus on the dart
shape before this time was up.

####  4.2.1. Stimulus

The stimulus for this experiment consisted of a dart-like target with three
circles, green (0 to 30 pixels radius), blue (30 to 60 pixels radius) and red
(60 to 90 pixels radius) as in Figure 3. Points within the center area i.e.
green have the lowest range of distances to the bulls-eye; each other co-
centric circle has a larger range of distance values. Any point lying outside
the three co-centric circular areas is considered as having a fixed maximum
distance of 90 pixels. For this experiment, a cross-hair icon was used.

Figure 3. Dart-based test stimuli: the accuracy is highest in the green area.
The cross-hair icon indicates the correct eye gaze location.

####  4.2.2. Measures

The purpose of this test was to measure the selected point’s distance on the
dart target to the center of the core circle (in green), thus the accuracy is
measured in pixels. Since the measured trials are chosen randomly, the average
is calculated to compare different methods based on accurate selection.

###  4.3. User Study 2: Ribbon-shaped Test

In order to compare our method to other studies, we performed the FittsStudy
(Wobbrock et al., 2011). This study is used to analyze pointing interaction
methods in accordance to well-established academic standards. As part of this
study, we measured three metrics to compare the performance of all interaction
techniques for point-and-select tasks, (1) throughput, (2) movement time and
(3) error rates for ribbon-shaped targets (see figure 4). We applied the
FittsStudy application
333http://depts.washington.edu/acelab/proj/fittsstudy/index.html by Wobbrock
_et al._ (Wobbrock et al., 2011). The test session includes three distances
(256, 384, 512) and two widths (96, 128) pixels.

###  4.4. User Study 2: Circle-shaped Test

This test is similar to the Ribbon-shapped test, however, contains different
target shapes. Figure 4 illustrates the screenshots of both test applications.
This experiment contains uni-variate endpoint deviation (SDx) through one axis
and bi-variate endpoint deviation (SDx,y) through both axes for throughput
calculations which results in better Fitts’ law model (Wobbrock et al., 2011).

Figure 4. Screenshots of the ’FittsStudy’ application (Wobbrock et al., 2011).
Top figure illustrates the ribbon-shaped stimuli and the bottom figure shows
the circle-shaped stimuli. The highlighted targets are shown in blue to
represent the active target to be selected.

##  5\. Results

To determine the effectiveness of the EyeTAP method, we analyzed the results
of our experiments using an analysis of variance (ANOVA) followed by
Bonferroni posthoc tests with the IBM SPSS software
444https://www.ibm.com/analytics/spss-statistics-software.

###  5.1. User Study 1: Matrix-based User Study

A two-way repeated measures ANOVA (methods $\times$ difficulty levels) was
performed to examine the effect of interaction type on: (1) completion time
and (2) path costs of target selection for each method and difficulty levels.

####  5.1.1. Completion time

We found a significant effect of interaction method on completion time
(F(12,384)=8.51, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between mouse ($M=8017.955~{}ms$, $SE=645.433~{}ms$)
and all other eye tracking methods (see figure 5). In addition, EyeTAP
($M=19998.812~{}ms$, $SE=2122.329~{}ms$), dwell-time ($M=11154.830~{}ms$,
$SE=788.395~{}ms$) and voice recognition ($M=26904.333~{}ms$,
$SE=2467.576~{}ms$) are significantly different (p ¡ .05). Figure 5
illustrates the average completion time per method for 8 targets per level
($\frac{40~{}targets}{5~{}levels}$).

Figure 5. Average completion time of point-and-select tasks for all
participants obtained from the matrix-based user study for 8 targets per level
($\frac{40~{}targets}{5~{}levels}$). Completion time was significantly
different for all techniques ($p<.001$).

####  5.1.2. Path costs of target selections

To examine the paths produced by selecting targets we compared the original
locations of the targets and the shortest path (ideal path model), as
described in Section 4. For each method, we had a $\frac{distance}{cost}$
measure to the shortest path. This metric can be regarded as the footprint of
each interaction technique on the display. A two-way repeated measures ANOVA
(methods $\times$ difficulty levels) showed that there was a significant
effect of interaction type on path cost (F(12,384)=2.57, p ¡ .05). A
Bonferroni posthoc test showed that dwell-time ($M=76.73~{}pixels$,
$SE=5.09~{}pixels$) produced the shortest path among all other interaction
techniques, even better than the mouse interaction
($M=109.25~{}pixels,SE=3.82~{}pixels$) with p ¡ .05. However, there is no
significant difference between dwell-time ($M=76.73~{}pixels$,
$SE=5.09~{}pixels$), EyeTAP ($M=84.80~{}pixels$, $SE=3.59~{}pixels$) and voice
recognition ($M=82.03~{}pixels$, $SE=4.41~{}pixels$). Figure 6, which shows
the path costs for all interaction methods, reveals that eye tracking
movements produce significantly lower movements than mouse on a large screen.

Figure 6. Mean path cost comparison calculated using the dynamic time warping
(DTW) algorithm. All eye tracking techniques have shorter path lengths than
mouse interaction for traversing items on a screen ($p<.05$).

####  5.1.3. Errors in target selections

To measure the effectiveness of each Midas touch solution we need to consider
a penalty for wrongly selected neighboring targets. Those targets are shown in
red on the screen (see figure 2). We projected the locations of errors per
each interaction method, since difficulty level 5 has the highest number of
targets (12 targets) on the screen, we illustrate the locations for this
difficulty level in Figure 7. EyeTAP has the highest number of errors, however
the figure reveals the potential regions of the screen which are more error
prone. As shown in the figure, most errors occurred from the center towards
the right side of the screen. In fact, the right side of the screen produces
more errors than the left side. Moreover, the lower side produces more errors
than the top side. Feit _et al._ showed that the same bottom and right regions
of the screen have lower accuracy (Feit et al., 2017). We confirm their
results and also demonstrate that the same regions are also more error prone.

Figure 7. The locations of errors during the matrix-based user study (figure
2) for difficulty level 5. The right side of the screen as well as bottom side
are more error prone than the left and top sides.

###  5.2. User Study 2: Dart-based User Study

We performed a one-way repeated measures ANOVA to compare the effect of the
different interaction methods on accuracy. The results of the ANOVA showed all
eye tracking methods have statistical difference (F(3,96)=104.92, p ¡ 0.001)
on selection accuracy. In fact, the mouse interaction has the lowest distance
to target (higher accuracy) compared to eye tracking techniques. EyeTAP
($M=45.11~{}pixels$, $SE=2.28~{}pixels$) achieved the highest mean pixel
accuracy compared to dwell-time ($M=35.30~{}pixels$, $SE=2.11~{}pixels$) and
voice recognition ($M=29.27~{}pixels$, $SE=2.07~{}pixels$). Figure 8 depicts
the results of the accuracy test.

Figure 8. The mean distance to target in pixels for dart-based experiment
($p<.001$).

###  5.3. User Study 2: Ribbon-shaped Test

A one-way repeated measures ANOVA was performed to examine the effect of
interaction type on: (1) movement time, (2) throughput and (3) error rates for
each interaction method.

####  5.3.1. Movement time

We found a significant effect of the interaction method on movement time
(F(3,96)=69.42, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between mouse ($M=684.15~{}ms$, $SE=16.80~{}ms$) and
all other eye tracking methods (figure 9). In addition, among all eye tracking
methods, dwell-time ($M=599.39~{}ms$, $SE=18.76~{}ms$) achieved significantly
lower movement time than EyeTAP ($M=1794.89~{}ms$, $SE=170.90~{}ms$) and voice
recognition ($M=2014.20~{}ms$, $SE=89.28~{}ms$) techniques. However, there is
no statistical significance between EyeTAP and voice recognition. The lower
movement time of dwell-time method compared to the mouse interaction is
associated with the low activation time (500 ms).

Figure 9. The calculated movement time per method for the ribbon-shaped test
($p<.001$).

####  5.3.2. Throughput

We found a significant effect of the interaction method on throughput
(F(3,96)=75.13, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between dwell-time ($M=3.30~{}bits/sec$,
$SE=0.36~{}bits/sec$) and all eye tracking methods (figure 10). The mouse
($M=4.81~{}bits/sec$, $SE=0.11~{}bits/sec$) achieved higher throughput than
the eye tracking methods. However, there is no statistical difference between
voice recognition ($M=1.15~{}bits/sec$, $SE=0.09~{}bits/sec$) and EyeTAP
($M=1.34~{}bits/sec$, $SE=0.12~{}bits/sec$).

Figure 10. The calculated throughput per method for the ribbon-shaped test
($p<.001$).

####  5.3.3. Error rates

We found a significant effect of the interaction method on error rates
(F(3,96)=27.15, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between mouse ($M=0.01~{}errors$, $SE=0.005~{}errors$)
and all eye tracking interactions (see Figure 11). In addition, dwell-time
($M=0.28~{}errors$, $SE=0.03~{}errors$) reached a higher error rate than
EyeTAP ($M=0.18~{}errors$, $SE=0.02~{}errors$) and voice recognition
($M=0.10~{}errors$, $SE=0.02~{}errors$).

Figure 11. The calculated error rates per method for the ribbon-shaped test
($p<.001$).

###  5.4. User Study 2: Circle-shaped Test

A one-way repeated measures ANOVA was performed to examine the effect of
interaction type on: (1) movement time, (2) throughput and (3) error rates for
each interaction method. This experiment is similar to ribbon-shaped test but
contains an extra metric to measure throughput of each method.

####  5.4.1. Movement time

We found a significant effect of the interaction method on movement time
(F(3,96)=67.48, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between EyeTAP ($M=1578.95~{}ms$, $SE=95.34~{}ms$),
dwell-time ($M=638.80~{}ms$, $SE=24.35~{}ms$), voice recognition
($M=2123.35~{}ms$, $SE=132.42~{}ms$) and mouse ($M=727.91~{}ms$,
$SE=46.12~{}ms$). However, there is no statistical difference between mouse
($M=727.91~{}ms$, $SE=46.12~{}ms$) and dwell-time ($M=638.80~{}ms$,
$SE=24.35~{}ms$). Figure 12 illustrates the mean movement time per method for
the circle-shaped test.

Figure 12. The calculated movement time per method for the circle-shaped test
($p<.001$).

####  5.4.2. Throughput

Since the circle-shaped test contains two variations (uni-variate, bi-variate)
to measure throughput (Wobbrock et al., 2011), we ran a two-way repeated
measures ANOVA (throughput $\times$ variation) and found a significant effect
of the interaction method on throughput (F(3,96)=19.75, p ¡ .001). A posthoc
Bonferroni comparison test showed a significant difference between mouse
($M=4.16~{}bits/sec$, $SE=0.18~{}bits/sec$), dwell-time ($M=3.20~{}bits/sec$,
$SE=0.25~{}bits/sec$), voice-recognition ($M=1.24~{}bits/sec$,
$SE=0.07~{}bits/sec$) and EyeTAP ($M=1.04~{}bits/sec$, $SE=0.13~{}bits/sec$).
However, there is no statistical difference between voice-recognition
($M=1.24~{}bits/sec$, $SE=0.07~{}bits/sec$) and EyeTAP ($M=1.04$
$bits/sec$, $SE=0.13~{}bits/sec$). Figure 13 shows both variations of
throughput per interaction method.

Figure 13. The calculated throughput for both uni-, and bi-variations per
method for the circle-shaped test ($p<.001$).

####  5.4.3. Error rates

We found a significant effect of the interaction method on error rates
(F(3,96)=18.25, p ¡ .001). A posthoc Bonferroni comparison test showed a
significant difference between mouse ($M=0.02~{}errors$, $SE=0.01~{}errors$),
dwell-time ($M=0.23~{}errors$, $SE=0.03~{}errors$), voice recognition
($M=0.13~{}errors$, $SE=0.02~{}errors$) and EyeTAP ($M=0.28~{}errors$,
$SE=0.02~{}errors$). Voice recognition ($M=0.13~{}errors$, $SE=0.02~{}errors$)
reached the lowest error rate among eye tracking methods, however, there is no
statistical difference between dwell-time ($M=0.23~{}errors$,
$SE=0.03~{}errors$) and EyeTAP ($M=0.28~{}errors$, $SE=0.02~{}errors$). Figure
14 illustrates the calculated error rates for the circle-shaped test.

Figure 14. The calculated error rates per method for the circle-shaped test
($p<.001$).

###  5.5. EyeTAP rating by users

We asked participants to evaluate the overall performance of EyeTAP in the
post-test questionnaire on a scale from 1 (worst) to 5 (best). EyeTAP reached
the average rate of 3.64 ($SD=0.99$) by 33 users. In addition, Users were
asked to select multiple interaction techniques. Figure 15 illustrates the
popular interaction techniques by users obtained from the post-test
questionnaire. EyeTAP reached the second desired eye tracking technique.

Figure 15. The recorded users’ multiple choice of interaction techniques among
33 participants.

###  5.6. NASA TLX scores

Figure 16 shows the NASA TLX scores for all interaction methods obtained
during the user study. The overall workload is the average of scale values
since we assume all scales equally important and therefore eliminated the
weighting calculations to apply a simplified version (Hart, 2006) of the basic
NASA TLX ratings (Group, 1986). According to our findings, the dwell-time
method has the lowest workload among other eye tracking techniques. However,
EyeTAP shows relatively lower workload compared to voice recognition
technique.

Figure 16. The NASA TLX scores for the interaction methods. (Left) Comparison
of each method based on different scales. (Right) The overall mean workload of
tested interaction methods.

##  6\. Discussion

Regarding the experiments with the reviewed Midas touch solutions, we found
several benefits and disadvantages of each method. We discuss each method
individually.

###  6.1. Voice Recognition

This interaction method showed relatively acceptable results but suffers from
some limitations. In general, a voice recognition engine depends on the user’s
voice, gender, language, and accent. Additionally, it is not applicable to
users with speech impediments. Another drawback is the need of prior training
samples to detect words correctly. Furthermore, similar words may lead to
false recognition as we experienced during our user study. The quality of the
microphone and its distance to the user is also another factor to be
considered for this kind of interaction. Regarding the accuracy of
recognition, the choice of recognition software plays an important role.
Finally, speaking out loud may not be suitable in certain working
environments.

In general, voice recognition presented some challenges for the users in terms
of wrongly recognized words, need for action word repetition, and delay
between input and feedback. The subjects’ rating of this technique was very
low (9.1%) in our user study. Voice recognition showed the highest completion
time in the matrix-based test and highest movement time in the circle-shaped
test and reached the highest cognitive workload among all interaction
techniques. However, voice recognition showed the lowest error rates in both
Fitts’ study experiments and reached the lowest distance to target (highest
selection accuracy) among other eye tracking techniques.

###  6.2. Dwell-Time processing

Dwell-time method showed the fastest completion time in the matrix-based test,
and fastest movement time and highest throughput in both Fitts’ experiments
due to the low amount of activation time (500 ms). In addition, it reached the
lowest amount of cognitive workload. However, it showed the highest error
rates in the ribbon-shaped test and with EyeTAP in the circle-shaped test.
Moreover, some users complained about eye fatigue after a while during test
sessions.

###  6.3. EyeTAP

We found several benefits of using EyeTAP in comparison to the other
interaction techniques. First of all, it has no dependent features, rather it
requires only an acoustic pulse (making sound with mouth) near a microphone to
send a signal. In fact, the output of EyeTAP in a noisy environment (up to
70dB) can appear deterministic after a number of repetitions. According to the
results of our study, it achieved faster completion time in the matrix-based
test, and faster movement time in the circle-shaped experiment than voice
recognition. In addition, it showed a similar path cost (pointer footprint on
display) with the other eye tracking techniques. It also achieved lower
cognitive workload in comparison to the voice recognition technique.
Furthermore, EyeTAP was the popular choice of interaction (36.4%) compared to
voice recognition (9.1%). However, EyeTAP showed relatively lower accuracy and
higher error rates than voice recognition, since most users had no prior
experiences with this kind of interaction. The performance of EyeTAP can be
improved with more training.

In general, EyeTAP is simple, integrates well into existing user interfaces,
and allows for easy and accurate point-and-select interaction because it
separates the actions of pointing and selecting to two different modalities
while relaxing the requirement for accurate voice recognition. The results of
our user study demonstrate that EyeTAP is a feasible alternative interaction
technique. Moreover, it is a robust and effective solution to the Midas touch
problem for eye tracking platforms and can be regarded as an alternative to
voice recognition technique.

##  7\. Conclusion and Future Work

In this paper, we proposed EyeTAP (Eye tracking point-and-select by Targeted
Acoustic Pulse), an eye-tracking interface that addresses the Midas touch
problem with acoustic input detection capabilities. EyeTAP allows for accurate
and effective interaction without the need for extra equipment or user
interface design for gaze-based interactions. The performance of the prototype
was measured in two independent user studies with 33 participants based on
eight criteria: (1) completion time, (2) path cost of target selection, (3)
error rate, (4) error locations on screen, (5) accuracy of target selection,
(6) movement time, (7) throughput, and (8) cognitive workload.

The results of our user studies showed that the dwell-time method outperformed
other eye tracking techniques, including EyeTAP on most criteria. At the same
time we found that EyeTAP, in comparison to to the other tested methods is a
competitive and a promising solution and provides a faster task completion
time, faster movement time and lower workload than voice recognition. In
addition, EyeTAP showed similar performance compared to the dwell-time method
and lower error rate in the ribbon-shaped experiment.

Moreover, our study showed that eye tracking has a lower footprint on the
screen compared to a mouse pointer in time scale. Additionally, we confirmed
that center regions towards the right and bottom side of the screen are more
error prone than the left and top sides. Additionally, we developed two user
tests that would be effective in studying different target selection for gaze-
based interaction techniques.

Although we only developed the left mouse click event, EyeTAP demonstrates a
completely hands-free or touchless alternative to mouse interaction for users
with disabilities and users who need to avoid physical contact with input
devices considering their workplace or situation. Thus, we believe EyeTAP can
be regarded as a competitive technique to both dwell-time and voice
recognition. In future work, we will apply the EyeTAP technique on AR/VR
headsets to measure its usability in different case scenarios.

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