Seeing our Blind Spots: Smart Glasses-based Simulation to
Increase Design Students’ Awareness of Visual Impairment
Qing Zhang

Keio University, Graduate School ofMedia Design
Yokohama, Japanqzkiyoshi@gmail.com

Juling Li

lijuling@mail.ustc.edu.cn
Keio University, Graduate School ofMedia Design
Yokohama, Japan

Giulia Barbareschi

Keio University, Graduate School ofMedia Design
Yokohama, Japan
barbareschi@kmd.keio.ac.jp

Yun Suen Pai

pai@kmd.keio.ac.jp
Keio University, Graduate School ofMedia Design
Yokohama, Japan

Yifei Huang

hyf@iis.u-tokyo.ac.jp
Institute of Industrial Science, The
University of Tokyo
Japan

Jamie A Wardj.ward@gold.ac.uk
Goldsmiths University of London
London, United Kingdom

Kai Kunze

Keio University, Graduate School ofMedia Design
Yokohama, Japan
kai@kmd.keio.ac.jp

Figure 1: The optical see-through Visual Impairment Simulation Glasses Concept and Prototype. (a) supposes a normal visionuser. (b) demonstrate the peripheral vision loss mode of our device. e) is the central vision loss mode. (c) & (d) are the simulated
peripheral vision loss from moderate to severe. (f) & (g) are the simulated central vision loss from moderate to severe.

ABSTRACTAs the population ages, many will acquire visual impairments. To
improve design for these users, it is essential to build awareness of
their perspective during everyday routines, especially for design
students.
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than theauthor(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from permissions@acm.org.
UIST ’22, October 29-November 2, 2022, Bend, OR, USA© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9320-1/22/10. . . $15.00
https://doi.org/10.1145/3526113.3545687

Although several visual impairment simulation toolkits exist in
both academia and as commercial products, analog, and static visual
impairment simulation tools do not simulate effects concerning
the user’s eye movements. Meanwhile, VR and video see-throughbased AR simulation methods are constrained by smaller fieldsof view when compared with the natural human visual field andalso suffer from vergence-accommodation conflict (VAC) whichcorrelates with visual fatigue, headache, and dizziness.
In this paper, we enable an on-the-go, VAC-free, visually impaired experience by leveraging our optical see-through glasses.
The FOV of our glasses is approximately 160 degrees for horizontal and 140 degrees for vertical, and participants can experience
both losses of central vision and loss of peripheral vision at different severities. Our evaluation (n =14) indicates that the glasses

UIST ’22, October 29-November 2, 2022, Bend, OR, USA

can significantly and effectively reduce visual acuity and visual
field without causing typical motion sickness symptoms such as
headaches and or visual fatigue. Questionnaires and qualitative
feedback also showed how the glasses helped to increase participants’ awareness of visual impairment.

CCS CONCEPTS• Human-centered computing → Interaction devices; Empiricalstudies in HCI .

KEYWORDS
visual impairment, smart eyewear, eye-tracking, aging vision
ACM Reference Format:
Qing Zhang, Giulia Barbareschi, Yifei Huang, Juling Li, Yun Suen Pai, Jamie
A Ward, and Kai Kunze. 2022. Seeing our Blind Spots: Smart Glasses-based
Simulation to Increase Design Students’ Awareness of Visual Impairment. In
The 35th Annual ACM Symposium on User Interface Software and Technology(UIST ’22), October 29-November 2, 2022, Bend, OR, USA. ACM, New York,NY, USA, 14 pages. https://doi.org/10.1145/3526113.3545687

1

INTRODUCTION

As the global population progressively ages, the number of people
with visual impairments will continue to grow. Individuals are likely
to experience some degree of vision loss throughout their lives [10].
However, public facilities are designed assuming normal vision. To
aid the understanding of visual impairments, several simulations
have been created such as analog glasses [27], static display-based
solutions [54], and video see-through AR glasses [48].
Despite the increasingly high prevalence of visual impairment
and numerous calls about the importance of ensuring that existing and novel technologies are accessible to people with varying
degrees of visual abilities, many designers remain unaware of theaccess barriers that they inadvertently introduce in the technologies
they develop [15].
Enhancing design students’ understanding of the barriers thatusers with visual impairment can serve to increase their generallevel of awareness, leading to a positive shift towards more inclusive
practices [42]. The most effective way to promote a comprehensive
understanding of how visually impaired individuals interact withtechnology and what their access needs are is to enable students
to interact with them directly [7, 67, 70]. However, facilitating direct interactions between students and visually impaired people
as part of educational programs for designers can be challenging
for instructors and burdensome for individuals with visual impairments [45, 75].
To help designers gain some basic understanding of the accessbarriers, several simulators have been developed utilizing a numberof different techniques [5, 38].
For simulations of visual impairment to be effective, they would
need to be able to mimic a variety of different conditions and adapt
to different situations and contexts [6, 22]. However, most existing
tools either focus on 2D images-based simulations, screen filters or
use normal glasses with tinted lenses or other non-electronic, static
tools [3, 27, 49, 63, 79]. The fidelity at which these basic tools cansimulate visual impairment is relatively low. The lack of adaptability
to gaze movement limits their applicability for more complex tasks,

Zhang and et al.

and the inability to simulate different degrees of visual impairment
could limit the user’s understanding of the diversity between andwithin specific conditions [1, 11].
Some researchers have leveraged the use of Head Mounted Display (HMD) simulations, mostly limiting the usage to virtual reality [12, 38]. To develop a realistic simulation of cataract, Krosl etal. [48] took advantage of video-see through HMD with real-time
eye tracking and involving ophthalmologists to create a more realistic simulation. However, using HMDs is still quite obtrusive dueto their high profile and weight [35]. Moreover, their application islimited to a virtual environment, whereas visually impaired people
perform their daily activities in the real world.
Particularly, accessing visual information through 2-dimensional
images changes the way our eyes naturally focus during activities
such as switching gaze from distant objects to closed ones and viceversa (e.g. depth perception and other visual properties are altered).
This results in less realistic simulation and potential motion sickness
symptoms [72]. Moreover, although high-end stereoscopic HMDs
can simulate depth in a way that resembles the spatial propertiesof the real world, at least currently, they are not able to replicate
how humans see and perceive depth under natural viewing conditions [72].
In this study, we focus on simulating (1) central vision loss and(2) peripheral vision loss, which are two typical symptoms of agerelated macular degeneration (AMD) and open-angle glaucoma.
To address the shortcomings of current simulation approaches we
present a new set of low-profile smart glasses that combines optical see-through displays with real-time eye-tracking that map thesimulation effect to correspond to the user’s gaze, which enables amore intuitive and effective simulation of visual impairment.
Our contribution is as follows: our device is the first lightweight
optical-see-through-based and real-time eye tracking integrated
visual impairment simulation solution that enables users to experience different types of visual impairments in everyday activities
in an unconstrained way, without significant obstructions to one’s
field of vision or the need for pre-defined virtual scenarios. Our user
study confirmed that our device significantly reduced the users’
peripheral vision and visual acuity respectively and increased theirawareness of visual impairments without causing typical motion
sickness symptoms such as visual fatigue and headache.

2 RELATED WORKS
2.1 Visual Impairment, Open-angle Glaucoma,
and Age-related Macular Degeneration
According to the 2019 World Vision Report by the WHO, visual
impairment occurs when one or more conditions of the eye affectthe visual system [59]. As a result, visual impairment can be associated with a reduction in visual acuity, the field of vision, contrast
sensitivity, or color vision [59]. Eye conditions that can cause visual
impairment are remarkably common with many people expected toexperience at least one of them throughout their life [59]. However,these conditions can vary in their severity and while some can becorrected through the use of surgical treatment or the use of assistive technologies, others lead to irreversible, and often increasing
degrees of visual impairment [59].

Seeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Glaucoma is one of the leading causes of blindness [9, 21, 44, 74],
and the number of people who suffer from glaucoma is increasing
worldwide [62]. Compared with cataracts, blindness caused byglaucoma is irreversible [44]. In particular, one of the most commonand severe types of glaucoma, open-angle glaucoma, is associatedwith the loss of peripheral vision as its main symptom, which canseverely impact individuals’ daily activities.
On the other hand, aging macular degeneration is a major causeof visual impairment in older adults [51]. As the average age of the
population in many countries continues to increase, it is expectedthat more than 300 million individuals might be affected by thiscondition by 2040 [55]. Although AMD does not cause complete
blindness, it is the most frequent cause of severe vision loss in older
age groups and it is associated with high rates of disability anddepression [64]. Aging macular degeneration causes losing central
vision, which means people suffering from this disease tend toperceive visual information from the center of the field of vision atlower saturation and even with distortion.
Open-angle glaucoma and AMD manifest in significantly different ways but are both associated with difficulties in Independent
Activities of Daily Living, in particular in regards to independent
mobility [31]. The combination of high prevalence and significant
impact on daily life makes open-angle glaucoma and AMD suitable
for a simulation exercise for design students as it could help themto understand the different access needs that people experiencing
these conditions might have [37].

2.2

Overview of visual impairment simulation
strategies

Several researchers have explored the direct sharing of subjective
experiences to communicate empathy, rational compassion, etc. andto provide intuitive assessment tools for people to understand eachother better [49, 57, 58, 65, 66]. In the case of visual impairment
simulation, there are three common approaches: analog tools, 2D
images-based simulation, and Head-mounted-Display-based methods (VR and AR). Table 1 presents a brief comparison of the main
characteristics of each type of simulator and our approach.
2.2.1 Analog visual impairment simulation tools. Analog tools (e.g.
static glasses, contact lenses, or other filters) are designed to simulate a specific visual impairment [27, 65] through an overlay effectwhile preserving our original vision as an information resource.
Goodman et al. simulated different degrees of blurry vision, using layers of Cambridge Simulation Glasses, to assess the visualclarity of product features [28]. Juniat et al. gathered 254 medical
students to complete three daily tasks while wearing Sim-specs,
which simulate Age-related macular degeneration (AMD) and glaucoma [39]. Their goal was to enable students to learn about visual
impairment. Kanzler et al. used "Produkt + Projekt” glasses as visual
impairment simulators and analyzed participants’ performance ina 40m obstacle walk to examine the influence of AMD on gait [40].
Czoski et al. use contact lenses to simulate AMD [16] with a similar
goal to one of the other researchers.
To mimic a variety of visual impairments, there are commercial
visual impairment simulators such as [19, 33, 61, 83]. In particular,
Zimmerman Low Vision Simulation Kit [83] provides four goggles

and several lenses which can simulate peripheral field loss, maculardegeneration, cataract, scotoma, and hemianopsia.
However, eye conditions such as cataracts and aging-related
macular degeneration, which lead to visual impairment happen in
the eye itself. This means that the resulting altered visual representation impairments will move along with the eyeballs’ movements.
The visual impairment simulation-generated analog tools did notrestore this attribute of our natural vision system, which inevitably
leads to less realistic simulation.
2.2.2 2D image based Visual impairment simulation. Amongst thedifferent types of software-based visual impairments simulators,
EASE (Evaluating Accessibility through Simulation of User Experience) offers a blurring and red-green color blindness experience [54].
Wood et al. simulated cataracts using Vistech light scattering filters to investigate the impact of visual impairment on older adults’cognitive performance [77]. McAlpin et al. [53] developed a mobile
device-based system simulating personalized CVD symptoms.
To emphasize the access needs of users who suffer from common vision or hearing impairments, [26] designed Windows-based
software which has the ability to adjust the degree of impairment
to different severity levels. Additionally, [17] presents a web pagebased simulator that mimics Blurry Vision, Ghosting, Glare, Halos,
Starbursting, Loss of Contrast, Visual Snow, Blue Field Phenomena,
and Trails. Similarly, [34] built a smartphone application that cansimulate several of the most common types of visual impairment
associated with different eye conditions.
Although 2D images-based methods are relatively easy to build
and low cost, their static simulation is simplified versions of thevarious visual impairments, which means they are not capable toreplicate realistically the specific situation of a visually impaired
person. Although the studies aforementioned enriched the toolboxof visual impairment simulation. They are limited to screen-based
interactions, whereas there are many more visual activities thatcover each essential aspect of our daily life. Finally, most typesof visual impairment are associated with a number of different
visual abnormalities. For example, in the case of age-related maculardegeneration, four typical attributes have been identified: distortion,
reduced saturation, reduced contrast, and darkened central vision;
which means studies based on existing 2D simulation images are
only able to provide simplified versions of visual impairments.
2.2.3 VR-based Visual impairment simulation. Ai et al. [2] created a
VR-based visual impaired simulation system for glaucoma, diplopia,
and aging-related macular degeneration. Their system provides
computational graphic stereo vision and head movement tracking.
Kim et al. presented Empath-D which is a VR-based visual impairment simulating a system with the goal of increasing empathy
towards access needs in the context of App design [43]. Furthermore, Krosl et al. mimicked reduced visual acuity (VA) throughblurring and had participants test the hypothetical effect of visual
impairment in an emergency situation [47].
With the goal of educating and training ophthalmologists, physical therapists, and students, [36] had their participants navigate
through a typical room in a virtual environment with simulated
vision impairment. Wu et al. simulated a pedestrian street crossing
scenario with aging-related macular degeneration [78]. However,
all these existing virtual environment-based simulations are faced

UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Zhang and et al.

with a significant trade-off between low-resolution and accessibilityof expensive equipment [48]. Moreover, VR simulations are limited
to a particular scenario of application that has been previously
created by the developer [38].
2.2.4 AR-based Visual Impairment Simulation. More recently, Krosl
et al. took advantage of video see-through AR and collaborated with
ophthalmology experts to create a more realistic cataract simulation
experience [48]. This helped to address the simulation accuracy
evaluation problem by having actual patients involved and theirsystem was also equipped with real-time eye tracking. However,even though the work ultimately targets augmented reality, theprototype and experiments are conducted in Virtual Reality using pre-recorded 360-degree videos. The objective of their study
was to quantify the influence of visual impairment on recognition
distances of escape-route signs.
To simulate various visual impairments, Ates et al. used videosee-through HMD to generate the typical visual impairment effects
such as Macular Degeneration, Diabetic Retinopathy, Glaucoma,
Cataracts, Colorblindness, and Diplopia [6]. Shen et al. [66] simulated common symptoms of dementia.
Nevertheless, even the latest high-quality stereoscopic video
see-through method or the virtual environment generally offers 2dimensional visual information with no natural depth clues on theflat displays, which leads to unnatural conflicts in visual processing that significantly differ from our actual visual perception [72].
Further, this temporary dissociation of vergence and accommodation has been linked to typical motion sickness symptoms such as
headaches and fatigue. One of the aims of this paper is to offer aversatile system that provides an initial empathetic understandingof visually impaired people by experiencing both central vision
loss and peripheral vision loss without experiencing dissociationof vergence and accommodation.

3

OUR APPROACH: TRANSPARENT LCD
WITH REAL-TIME EYE TRACKING

As stated by Ates et al. [6], visual impairments are subjected toa large amount of inter-individual variation, which pointed out
the necessity to simulate different types of visual impairments anddifferent degrees of severity for each condition as realistically as
possible. In this paper, we focus on the simulation of open-angle
glaucoma and age-related macular degeneration (AMD).

Visual Impaired Simulation System
We used two monochrome 2.9-inch (55 mm x 55 mm viewing area,
128 x 128 pixels) liquid crystal display panels1 as our optical seethrough lenses, which were inspired by Hiroi et al. and Ma etal. [30, 52, 80, 81], for the generation of a semi-transparent layer inbetween the real world and our eyes, as illustrated in Figure 5. The
LCD modules ensure the generated visual impaired effects cover the
majority of a person’s most natural visual field like ordinary opticalglasses, see figure 3. Although wearing glasses will reduce parts ofour visual field, in particular one’s peripheral vision, GAUTHIER etal. [23] suggests we have the ability to fast adapt to that narrowed
visual field and react as usual.
1 JHD128128-G56BFW-G

Figure 2: A. depicts the assembled Visual Impairment Simulation Glasses with two extra elastic bands to fasten the device
to the user’s head minimizing undesired displacement. B.
indicates the original Pupil CORE eye-tracker. C. is the extension kit that we designed and 3D printed for modifying
Pupil CORE into Real-time Visual Impaired Effects Rendering Glasses.
We designed semi-transparent patterns to be shown on the LCDs
to be able to simulate two visual impairment symptoms; loss ofcentral vision and loss of peripheral vision. Loss of central vision
is represented as a filled circle with a transparent peripheral fieldof view. On the other hand, loss of peripheral vision is shown as atransparent circle with a filled peripheral, see figure 5. The severity of the vision loss is controlled over the opacity setting of thefilled region. Based on consideration of generalizability, we use
filled circles and center-transparent patterns to simulate loss of central vision and loss of peripheral vision. Since visual impairments
can vary greatly between individuals [43], our visual impairment
simulation glasses enable a high degree of customization to better
simulate various situations, see figure 5. To allow others to replicate
the hardware, we provide the 3D models of our device2 .
Real-time Gaze Tracking and Mapping. Since visually impaired
effects such as loss of peripheral vision and loss of central vision
are gaze-dependent [48], we use Pupil Core Eye Tracker [41] toobtain the eye movements as well as to generate mapping clues forthe corresponding visual impaired effects. Pupil CORE has been
2 https://github.com/qzkiyoshi/visual-impairment-simulation-glasses

Seeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USA
Visual Impairment Simulators

Central and Peripheral Vision Loss

Visual Acuity

Eye Tracking

VAC Free

WeightFOV

Real-world Adaptive

Analog Tools [19, 27, 33, 61, 65, 83]✓
Natural visionX✓≥ Normal glasses≤ Normal glasses✓
2D image-based simulation [17, 54, 77]✓
Limited resolution
✓
N/A
N/A
N/A
X
VR-based simulation [36, 43, 47, 78]✓
Limited resolution✓△
468g [76]≈109°(H),112°(V) [56]X
AR-based simulation [48] (VST)✓
Limited resolution✓△
550g [76]≈107°(H),108°(V) [56]Pre-design Needed
Our simulation Glasses (OST)✓Natural vision
✓
✓
89.6g
≈ 160°(H),140°(V)✓Note: (VAC) is short for vergence-accommodation conflict. ( △) meant no computational heavy nor optical VAC-free method was implemented. (VST) is short for Video see-through. (OST) stands for opticalsee-through. In the Weight column, the weight that we used to represent VR-based simulators is the minimum in their groups. The weight of our OST visual impairment simulation glasses only counts the glasses
themselves (Pupil CORE + LCDs + 3D printed extension kit + wires). In the FOV column, the horizontal and vertical visual fields that we used to represent the VR and AR-based simulators are the maxima in theirgroups.

Table 1: Comparison of related studies and commercial products.

Figure 4: Three basic modes of the visual impairment simulation glasses.

Figure 3: (a). indicates the horizontal vision field of our visual
impairment simulation glasses. (b). indicates the verticalvisual field. (c). is the lenses-width comparison between ourvisual impairment simulation glasses and ordinary myopia
glasses, in which orange dash lines are the outermost edges ofthe ordinary myopia glasses, and the green lines indicate theoutermost left and right boundaries of our visual impairment
glasses.

widely used in various studies because of its robust eye-tracking
performance and lightweight (22.75g3 ) [20, 29, 80, 82]. The specific
type of Pupil CORE that we used consists of two 200Hz 192x192
pixel infrared cameras (8.5ms latency) and a 120Hz 480p World(Front) Camera ( Latency is typically greater than 3ms, CPU depending) [50]. To drive the Pupil CORE, we followed the official
requirements of Pupil lab (Intel i5 or greater, 8GB or more RAM) andused a laptop equipped with Intel i7 CPU( 11 Generation) and 16GB
RAM (ddr4 3200MHz). This configuration allowed us to reduce the3 https://pupil-labs.com/products/core/tech-specs/

delay between the eye movements and the generated corresponding
visual impaired effects to approximately 100ms.
The LCD displays are controlled through a SparkFun ESP32
ThingsPlus4 with a USB cable connecting to the laptop aforementioned to minimize potential data transmission delay. We designed
two resin frames to hold the two LCD lenses in between the eyes andthe Pupil Core eye-tracking cameras. To ensure that the eye movement tracking cameras could detect eyeball movements successfully
and stably through the LCD panels, we replaced the original camera arms with our re-designed and 3D printed ones, see figure 2.
This modification enabled us to set the cameras at an optimal angle
to detect the pupils’ movements successfully. Moreover, we also
designed an LCD connector/ fixer to join the two LCD modules
together to enable successive and stable visual impaired simulation.
Finally, to minimize the potential displacement of the visual impairment simulation glasses, we attached two elastic bands to the endsof the legs of the Pupil CORE. To drive the LCD modules as well as
to minimize the size and weight of the prototype, we designed thedriver board based on the manual of the LCDs manufacturer.

Benefits and Limitations
The advantages of our approach: (1) instant real-world adaptability:
our approach enables the user to experience visually impaired vision
instantly in real-world scenarios. Users can check whether a visualdesign is low-vision friendly or not by simply switching on and off
the visually impaired modes, as well as checking if an environmental
design is inclusive for visually impaired people. While analog visual
impairment simulation tools and 2D image-based methods offer
only static effects and are unable to respond to eye movements.
Current HMD-based methods need a pre-designed environment
such that the user cannot instantly interact with real-world visual
stimuli. (2) VA-conflict-free: our lightweight optical-see-throughdesign avoids the vergence-accommodation conflict which links tovisual fatigue, headaches as well as serious side effects long after
cessation. They are parts of the main challenges in HMD-based
4 https://www.sparkfun.com/products/15663

UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Figure 5: (a). are the generated peripheral vision loss effects
from mild to severe. (b). simulates the overlapped effects withthe real-world scene. (c). are the generated central vision loss
effects from mild to severe. d. simulates central vision loss
effects.

Figure 6: Hardware’s working flow: Pupil CORE detects eye
movements and sends them to the data processing laptop,
the laptop is running a Python script that converts eye movements into x and y coordinates to the attached Esp32 thingPlus microcontroller that generates the visually impaired
effects to the LCD lenses.
systems [46]. (3) versatility: our programmable LCD-based andmodular design suggests we can not only simulate loss of peripheralor central vision, but can also simulate other impairments such as
Retinitis Pigmentosa, Floaters, Cataracts, or even rare and complex
cases such as people experiencing multiple visual impairments (e.g.
age-related macular degeneration in amblyopic). (4) walkable: alike
our experiment suggests, one can walk up doing their daily routines
while wearing our device when accompanied, which enables the

Zhang and et al.

further realistic and immersive experience of visual impairments.
(5) computationally simple: our method does not require heavy
computational power which is crucial for high-resolution renderingand complex calibration that is needed in HMD/HWD-based studies.
(6) optical-glasses-like field of view: our device can reach the largest
field of view (160° horizontal, 140° vertical) when compared to therelated AR & VR-based studies and the mainstream commercial
HMDs (Oculus QUEST 2 (104° horizontal, 98° vertical), Varjo XR-3
(115° diagonal)).
In terms of other optical see-through devices that might potentially be used as visual impairment simulation platforms such as
HoloLens2 or MagicLeap2. First, HoloLens2 can only generate additive RGB mixing-based luminous pixels, while our device generates
black/ opaque pixels which are more suitable to simulate the degenerated macula or affected optic nerves. Additionally, MagicLeap2’s
dimming layer can only be adjusted wholly, while our device canadjust each pixel individually.
Limitations. Our optical glasses-like design suggests that thiscurrent prototype could not completely cover all of the wearer’svisual field, especially on the left and right sides. Although, with ahorizontal visual field of 160 degrees and a vertical visual field of
140 degrees, our OST visual impairment simulation glasses ensuresignificantly better coverage than most available HMD [56], seetable 1.
Although our visual impairment simulation glasses (without
counting the laptop’s weight) were significantly lighter than popular HMDs, it was heavier than ordinary glasses, which requires
further lightweight design modification. Another notable hardware
limitation was linked to the refresh rate of the current monochrome
LCD and the sampling rate of the pupil labs eye tracker (up to 250
Hz). The LCD and eye-tracking setup is currently not fast enoughto match the speed of natural eye movements. As described in
the qualitative feedback section, the lag for larger saccades wasnoticeable, about 100ms. Yet, this issue also persists for software
approaches using eye-tracking and HMD-type simulations.
Regarding the reliability of our simulation, we observed oursimulated peripheral vision loss effect could not be seen by one
participant (1/14), which indicates the individual variations (noseheight, interpupillary distance, head circumference, ears’ place)among the participants could have made certain individuals see
through the polarizers of the LCDs. We believe that future versionsof this OST visual impairment simulation glasses will overcome
this demerit by leveraging specifically designed polarizers or moresuitable transparent display modules.

4 USER STUDY
4.1 Participants
We recruited 14 graduate students who were currently enrolled
in either the MSc or Ph.D. program. Participants were recruited
through a combination of departmental emails, student message
boards, and word of mouth. Six of them identified themselves asmale and eight as female. They were on average 26.9 years old (SD= 3.7, Min = 22, Max = 34). Seven participants were nearsighted orfarsighted. Amongst them, one participant was nearsighted in theright eye and farsighted in the left. Three of the seven nearsighted
participants wore their contact lenses throughout the experiment.

Seeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USA

for the full details of the task. Once the personal item search task
was successfully completed, the participants were asked to undergo
either the visual acuity test (if the central vision loss effect was
been applied as the first condition) or the field of view test (if theperipheral vision lost test was being applied as a first condition).
Participants underwent the selected test twice, once with the glasses
set in full transparent mode, and once with the glasses displaying
the appropriate visual impairment filter (the order between thesetwo conditions was randomized among participants).
After completing the first either central vision loss or peripheral vision loss test, participants were asked to perform anotherpersonal item search task with the opposite visual impairment filter
applied followed by the remaining corresponding vision test. At
the end of the study, participants were asked to fill in the empathy
questionnaire again, followed by open questions to elicit additional
feedback. The complete Experimental procedure is summarised infigure 7.

Figure 7: A diagram summarizing the complete procedure of
the user study.
On the other hand, the remaining four took part in the experiment
without wearing corrective glasses as they self-reported that they
can do their daily routines without wearing glasses. Participants
were compensated with a gift card for their time.

4.2

Study procedure

After obtaining informed consent and collecting demographic information, participants were asked to fill in a baseline questionnaire
assessing their general awareness and empathy in relation to individuals with vision loss (see the following subsection for details
about the questionnaire design). Before the start of the experiment,
the researchers helped the participants to put on the device andfastened the yellow elastic bands (see figure 2) behind the participants’ heads minimizing the undesired displacements of the smart
glasses for the duration of the study. The order of the two visual
impairment effects, central vision loss, and peripheral vision loss
were randomized among participants, while the overall study procedure remained consistent. After confirming that the device washeld in place stably and correctly, the calibration was carried out
to ensure that the chosen first visual impairment simulation effectwould be located in the appropriate region of the participant’s fieldof view was initiated using the GUI operating system.
After the end of the calibration of eye tracking and the corresponding place of pupils on the displays, the participant was asked
to navigate around the room to locate their mobile phone which
had been previously hidden by the experimenter. See section 4.2.4

4.2.1 Questionnaire Design. One goal of our user study was toevaluate if the simulated experience provided by the smart glasses
could effectively increase design students’ awareness of visual impairment and the importance of accessibility, leading to greaterinterest to interact with visually impaired users. Although many
disability simulation tools claim to be designed with the goal toincrease empathy amongst designers, their impact in this regard
is not often specifically assessed [6, 11, 69]. Most available scales
that assess empathy or attitudes toward people with disabilities are
not specific to designers and do not necessarily provide insights
on shifting priorities in relation to accessibility and future design
practices [25, 45].
More recently Drouet et al. 2022 [18] have developed an 18item Empathy in Design Scale to assess the empathetic tendency
of designers and stakeholders toward particular groups of users
along four different dimensions Emotional interest / Discovery,
Sensitivity/ Immersion, Personal experience/ Connection, and Selfawareness/ Detachment. To ensure that design students remained
aware that while the glasses could provide an experience of visual
impairment they could not, and indeed should not [8], create atruly embodied experience of disability we removed the sections
on Sensitivity/ Immersion and Personal experience/ Connection
from our evaluation. Furthermore, the original scale measures overall empathetic tendencies, whereas our study design could only
assess the change in the empathetic state before and after the simulation. To this end, we modified the phrasing of the questions torepresent one’s state of mind at a specific moment in time rather
than a more generic personal inclination. The result was an 8-item
questionnaire, in which participants could express their level ofagreement or disagreement with each statement using a 5-point
scale. In particular, question 1 to 4 assesses the potential changeof "Emotional Interest/ Discovery (EI)", whereas question 5 to 8
examined the "Self-awareness/ Detachment (SA)" level [18]. The
questionnaire was administered to participants at the start of thestudy and after they completed the experiment.
Additionally, to assess whether our device could simulate central
vision loss and peripheral vision loss without triggering typicalmotion sickness symptoms, such as headaches and visual fatigue,
which have been associated with other simulation strategies [13],UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Zhang and et al.

Questionnaire:
1. I am interested to learn about the experiences and needs of people with vision loss.
2. I imagine how people with vision loss think, feel or behave in different situations.
3. I am curious about the experiences and needs of people with vision loss.
4. I want to learn about the people with vision loss’s experiences and opinions about my research and design work.
5. I imagine how I would feel and think if I were a person with vision loss rather than a person without vision loss.
6. I am aware that my experiences as a person without vision loss are different from the ones of people with vision loss.
7. I realize that there are similarities and differences between my experiences and the ones of people with vision loss.
8. I understand why people with vision loss perceive things differently than I do as a researcher and designer.
MS.1. When using VR headsets I experience headaches and/or visual fatigue.
MS.2. I experienced headaches and/or visual fatigue when switching my gaze from distant objects to closed objects orvice versa while wearing the glasses.
Table 2: The 5-scaled Likert answers are: Strongly Disagree/ Disagree/ Neither Agree or Disagree/ Agree/ Strongly Agree.

we added two items to the questionnaire that was administeredafter the simulation experience. The full questionnaire used in theuser study is shown in Table 2. At the end of the study, participants
were asked for any additional feedback they wanted to share about
the use of the simulation glasses and how the effect of the visual
impairment simulation experience.
4.2.2 Loss of Central Vision Simulation. The aim of the loss ofcentral vision test was to evaluate if the simulated effect created byour glasses could effectively reduce the visual acuity of participants.
This particular measure was chosen as loss of visual acuity has been
identified as a strong indicator of age-related macular degeneration(AMD) [60].
When applying the central vision loss effect we refer to theestimation provided by previously related studies [4, 32, 68] whichclassifies the median 10 degrees of the visual field as the central
vision area. However, it is worth noticing that there are some othervariations in regards to the definition of central vision [71].
To conduct the visual acuity test we had participants sit downon the chair we placed 10 feet away from an A4-sized Snellen Chart.
The visual acuity test was conducted twice consecutively, oncewith our device in its fully transparent mode and the second timewith the central vision loss filter applied. Both tests were conductedwith participants using their binocular vision. Although the Snellen
chart is usually used to test the visual acuity by blocking one eye
at a time, since our aim was to examine whether the central vision
loss mode of our visual impairment simulation glasses could reduce
the general (binocular) visual acuity, we chose to perform the testwith the participant using binocular vision.
To minimize the undesired influence of learning effects frompast experience, and considering the Snellen chart’s popularity, weused two character-shuffled charts that rearranged the order of thecharacters while maintaining their characteristics including type,
font size, and layout design, see figure 10. We also shuffled the orderin which Snellen Chart was used for each condition.
4.2.3 Loss of Peripheral Vision Simulation. The loss of peripheral
vision test was to evaluate if the simulated effect created by our
glasses could significantly reduce the visual field of participants.
This particular measure was chosen as the reduced visual field isa strong indicator of open-angle glaucoma and retinitis pigmentosa [24]. To explore the changes in the peripheral vision area ofthe participants, we designed a 5 Processing program which is used
to generate dots from 8 directions of the display edges (randomizedorder, upper right, up, upper left, left, right, bottom left, bottom,
bottom right) one by one towards a red cross that located in thecenter of the display. This program mimicked the automated static
perimetry test which is commonly used to assess the individual
field of vision.
During the test, participants were required to sit at a chair as
figure 8 indicates, in front of a 65 inches IPS display. The distance
between the display and the participant’s head was fixed to 25
cm by an adjustable chin rest. This distance was calculated using
trigonometric functions to make sure the display area was sufficient
to cover the participant’s peripheral vision vertically and horizontally. To correct for individual variations of the field of view, we
asked participants to self-report whether the display area well coincided with their visual field. If this was not the case, we slightly
adjusted the distance between the display and the chin rest until
they confirmed that the horizontal and vertical boundaries of theirvisual field were within the four sides of the display. Notably, thevisual field that we expected the display to cover was the point ofview from the two LCD lenses.
When the appropriate chin rest position was determined the
peripheral vision test was carried out with two conditions in randomized order. Once with our device and in its fully transparent
mode, see figure 4a, and another is the peripheral vision loss mode,see figure 4c. To minimize fatigue, a two minutes break was introduced in between the two conditions.
When applying the peripheral vision impairment filter, the experimenter made sure that the generated effects were aligned withthe pupils of the participant, as well as examining that the visually impaired effects would move correspondingly with the eyes’
movements. This eye-tracking examination enabled the generated
effects would act along with microsaccades, which resulted in amore realistic intuitive simulation of visual impairments.
During the field of vision test, participants were required to fix
their gaze on the red cross located in the center of the display and
5 Processing: Homepage (https://processing.org/)

Seeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Figure 8: Participants were required to sit in front of the 65
inches display and gaze at the center where the processing
script generates a red cross.

asked to press a button as soon as they noticed a white circle approaching out of their visual field towards the red cross. We usedthe custom-made processing script to display the white dots 10
times, shuffling their order of approach across 8 directions (upperleft, up, upper-right, left, right, bottom-left, bottom, bottom-right).
The first two trials were used as non-recording baselines. The approaching speed of the dots is 15 frames per second. The button
that participants were asked to press was placed around their righthand. To avoid false triggering, participants were asked after theypressed the button to make a hand sign pointing in the directionof the perceived dot, which means only correct triggering would
be recorded. Participants were informed that blinks were allowed
during the tests.
4.2.4 Personal-item-search tasks. Since visually impaired symptoms are generally not of short-time duration but long-lasting experiences, most visually impaired people who are affected by theloss of central vision or peripheral vision, will likely carry out their
daily routines experiencing either narrowed visual field or reduced
visual acuity and contrast. Thus, to better represent and simulate
the experience of losing central vision and losing peripheral vision,
we had participants perform two personal-item-search tasks in theexperimental environment before the visual acuity and peripheral
vision tests, utilizing the corresponding visual impairment effect.
Before the beginning of each task, participants were informed by
the experiment that they did not need to uncover anything to locate

Figure 9: From P1 to P14 are the results of both transparent
mode and peripheral vision loss mode. Polygons in lightgreen color are the visual field of the transparent mode.
Khaki polygons indicate the results of the peripheral vision
loss mode. The semi-transparent pink polygons.

their phone, nor were they allowed to use other wearable devices
such as a smartwatch or an AirTag to activate their smartphoneor by voice. As part of the task, they could freely walk around theroom to find their smartphone which had been previously hiddenby the experimenters. To challenge their ability to recognize objectsof similar nature, along the potential search paths, the experimenter
placed 3 different smartphones in different locations.
During the personal item search task, we prioritized the safetyof the participants while maintaining our goals to increase awareness of visual impairment. Two experimenters accompanied theparticipants from behind ( back left and back right) making sureno injury would occur as a result of accidental collision. One of theexperimenters held the laptop to run the visual impairment simulation system and ensure that the USB cable connection between
the simulation glasses and the laptop was not tense, to minimize
unnecessary discomfort.

4.3

Result4.3.1 Changes in peripheral vision loss mode. In the most cases, weobserved reduced peripheral vision in in the peripheral vision loss
mode of our device. On average, the upper half of the participant’s

UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Figure 10: Two re-designed Snellen Charts maintain the original visual design. (a). indicates the central vision loss condition, (b). reveals the result of using the transparent mode ofour visual impairment simulation glasses.
field of vision was more affected by the presence of the peripheral
loss filter compared to the lower half. Specifically, we observed astatistically significant decreased field of vision from 6 out of all 8
directions including upper left (p<.0046), up (p<.0076), upper right(p<.006), right (p<.0001), bottom left (p<.0017), and bottom (p<.04).
Participants’ field of vision was also decreased by the application of
the peripheral vision loss filter in the left and bottom right direction,
but these differences were found to be not significant (p= .15, and
p=.25 respectively). Notably, in the case of P4, we found a opposite
result compared with others. We observed almost no significant
difference in the field of vision between full transparent mode andperipheral vision loss mode in the case of P8.

Figure 11: Visual acuity changes of central vision loss simulation. VA Loss is the result of central vision loss mode. TRANS.
indicates the result of using transparent mode.
4.3.2 Changes in central vision loss mode. The application of thecentral vision loss filter caused a reduction in the visual acuity of allparticipants as shown in Figure 10. On average, with the simulation
glasses set in the transparent mode, participant visual acuity was
7.7 (max 10, min 2), with 10 out of 14 participants scoring between
8 and 10. On the other hand, when the central vision loss effect
was applied, average visual acuity was reduced to 5.5 (max 8, min2), with the majority of participants scoring between 6 and 8, see

Zhang and et al.

Figure 12: MSQ1. is the result that reveals the baseline VR
headsets experience of the participants. MSQ2. suggests thatour visual impairment simulation glasses could simulate thevisually impaired effects without triggering headaches andvisual fatigue.
figure 11. The decrease in visual acuity was found to be statisticallysignificant using a paired t-test, p <.0001.
4.3.3 Awareness and empathy. When examining the impact of thesimulation on the "Emotional Interest/ Discovery" and the "Selfawareness/ Detachment" of participants towards individuals withvisual impairment, the comparisons of pre and post-study questionnaires showed a statistically significant increase in both dimensions.
Table 2 and figure 13 show that for all items included in the questionnaire, participants reported a significant positive shift in awarenessand empathy. In particular, 8 participants stated that after taking
part in the study they had more interest in learning about the experiences and needs of people with vision loss (Q1); 8 participants
were more likely to imagine how people with vision loss would
think, feel or behave in different situations (Q2); 9 participants had
an increased interest in learning about the experiences and opinionsof people with vision loss’s in relation to their research and design
work (Q4); 8 participants were more likely to imagine how theywould feel and think if they were a person with vision loss (Q5),
and 8 participants had increased awareness of the similarities anddifferences between their own experiences and the ones of people
with vision loss (Q7).
4.3.4 Motion sickness symptoms. When asked whether the visual
impairment simulation provided by our glasses ever triggered typical motion sickness symptoms such as headaches and or visual
fatigue. 9 out of all 14 participants answered either Strongly Disagree or Disagree, while two answered Neutral, and three answered,
Agree. To further enquire about the potential causes of their discomfort, we asked participants about the specific effects or tasks
that triggered their headache and or visual fatigue, and if theseSeeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USA

Figure 13: Comparison of pre and post-questionnaires. Fullquestions can be found in table 2.
unpleasant signs lasted as long as they usually experience whenusing VR. Two participants stated that the visual fatigue occurred
as a result of the presence of the central dots (central vision loss
effects), which made them spend more energy seeing things’ details.
In turn, they mentioned that the central dots led to the feeling ofvisual fatigue, but no headaches were experienced. Moreover, it wasadded that as soon as the smart glasses were removed the visual
fatigue disappeared immediately.
On the other hand, when talking about the headache and orvisual fatigue ever experienced in connection to the use of VR
headsets, 11 out of 14 participants answered either "Strongly Agree(8)" or "Agree (3)". Of the remaining 3 participants, two had nevertried wearing VR headsets and 1 answered strongly disagree. See
figure 12.
4.3.5 Qualitative Feedback. In terms of the oral feedback that we
received from the participants, we identified contrasting opinions
regarding their impaired central and peripheral vision experience.
Concerning the personal-item-search task, half of the participants
stated that the impaired peripheral vision caused more challenges,

whereas the other half found the impaired central vision condition
more difficult. Amongst the people who mentioned peripheral vision loss as most challenging, the majority stated that it was notvery difficult for them to quickly scan the area even when their central field of vision was covered by the central vision loss effects. On
the contrary, among participants who found the impaired central
vision most difficult, the majority stated that it was challenging todistinguish details of the surrounding objects when their central vision overlapped with the effects. Moreover, they felt that the clarityof the environment would also decrease.
Interestingly, one participant stated that the impaired peripheral
vision helped her focus more on the personal item searching tasks.
However, all participants agreed that both conditions narrowed
their vision and reduced their confidence in walking around afamiliar environment. During the visual acuity test with the Snellen
charts, all participants mentioned that clarity was significantly
reduced when the central vision impaired mode was active. In
particular, it was difficult for them to distinguish small details andminor differences. For example, they felt frequently confused by
distinguishing E and F, F and P, and O and C in the Snellen Charts.
When comparing the differences between VR headsets and the
OST visual impairment simulation glasses used in this study, participants unanimously stated that they found this device offereda more comfortable experience. In particular, none of the participants experienced dizziness as a result of using the OST visual
impairment simulation glasses, which were frequently mentioned
in connection to VR HMD use. Moreover, many of our participants
mentioned that the simulation provided by our glasses made theexperience feel more natural and gave them a better experience
than just placing a screen in front of their eyes.
One of the participants has a fascinating take on the differences
between VR and our glasses. She mentioned that the premise between VR headsets and our smart glasses was for her entirely different.
Based on the oral feedback from the participants it was also clearthat the awareness of vision loss increased. One of the participants
told us that she didn’t put much consideration into vision loss
before the experiment. However, after our experiment, she reallyfelt frustrated and annoyed by the barriers faced by people withvision loss.

5

DISCUSSION

Our experimental results show that our method can statisticallyand significantly reduce visual acuity and field of vision, which
are the key factors when defining visually impaired levels. While
relevant works such as [14] & [73] highlight how static filters andsuperimposed black occlusions on standard spectacles have beenreported as unrealistic by individuals with both glaucoma and AMD.
Our approach of using programmable transparent LCDs to simulate visual impairment represents a more real-world adaptable andintuitive way to capture the variability of visual impairment symptoms. Key hardware features of our prototype include adjustableseverity of visual acuity loss by altering the contrast level of thesimulating patterns, while also can control the size and location
to represent the affected area of the vision system. The further
advantage of incorporating eye-tracking means that the simulated

UIST ’22, October 29-November 2, 2022, Bend, OR, USA

effect follows the gaze of the individual, providing a more realistic
experience. The result of the personal item searching tasks alsosuggests the potential that our optical see-through visual impairment simulation glasses can support daily routine-like tasks easily.
The observed hesitation that happened when the participants weredistinguishing whether the detected phone is a dummy or their own
phone indicates the reliable and stable visually impaired simulation
experience that our method can offer.
In the qualitative feedback section, we found participants who
had negative VR experiences are likely voluntarily to draw a line
between our device and HMDs, they could have rejected to join
our study if we need them to try on an HMD for the experiments.
Additionally, they categorized our device as either glasses or AR
glasses, none of them used either HMD (head-mounted display) or
HWD (head-worn display) to describe our prototype. It suggeststhat the appearance design and the visual information providing
mechanism (OST or VST) may correlate with the acceptance leveland subjective categorization.
Results from the empathy scale show that experiencing our
glasses had a positive impact on the level of awareness of design
students, in particular increasing their willingness to engage withindividuals with lived experience of visual impairment.
Regarding how accurate our system can simulate, despite ourbest efforts, overall it is very difficult to know if a simulation mimics
a subjective experience of a person with visual (or other) impairment [33]. Thus we chose to leverage programmable optical seethrough displays as our lenses to approach the individual variationand different severities.
In the next step, we plan to evaluate if our visual impairment
simulation glasses can provide a sufficiently good approximation
for peripheral and central vision loss modeling of the accuracy andefficiency loss in standard vision tests (similar to Godman et al.’s
experiments with static glasses [27]).

6

CONCLUSION

In this paper, we introduced the first optical see-through (OST)visual impairment simulation glasses with real-time eye tracking.
These glasses enabled significantly reduced visual acuity and visual
field without causing typical motion sickness symptoms such as
headaches and or visual fatigue. By experiencing these OST visual
impairment simulation glasses to do daily routines, the wearers
showed significantly increased awareness and empathy for thevisually impaired experience.
Unlike analog visual impairment simulation goggles, 2D imagebased simulation applications, and video see-through HMD-based
visual impairment simulation methods, our OST visual impairment
simulation glasses have the largest FOV and enable a VAC-freevisual information processing experience.
We conducted two visual impairment simulations, one is central vision loss, and the other is peripheral vision loss. During theexperiment, participants experienced two personal item searchingtasks under those two visually impaired simulations one to anotherfollowed shifted order. To address the potential changes in bothawareness and empathy after the visual impairment simulation, we
had the participants fill out a pre and a post questionnaire, the prequestionnaire was filled out before the simulation started, and the

Zhang and et al.

post-questionnaire was filled out after all the two visually impaired
experience. We found our optical see-through visual impairment
simulation glasses enabled significantly visually impaired experience for both reduced visual acuity and visual field. Combining
the personal item searching tasks, we found significantly increased
awareness and empathy for visual impairment.
Altogether, we find our visual impairment simulation glasses
are the first optical see-through based method that enabled VACfree visually impaired experience and can be real-time and realworld adaptive for various simulating scenarios such as doing daily
routines and checking if a product’s or an environmental design isfriendly enough for visually impaired peoples.

ACKNOWLEDGMENTS
We appreciate the anonymous reviewers for their practical andvaluable comments. This work was conducted under the IoT Accessibility Toolkit, supported by JST Presto Grant Number JPMJPR2132.

REFERENCES[1] Samuel Aballéa and Aki Tsuchiya. 2007. Seeing for yourself: feasibility study
towards valuing visual impairment using simulation spectacles. Health economics
16, 5 (2007), 537–543.
[2] Z. Ai, B.K. Gupta, M. Rasmussen, Ya Ju Lin, F. Dech, W. Panko, and J.C. Silverstein.
2000. Simulation of eye diseases in a virtual environment. In Proceedings of the
33rd Annual Hawaii International Conference on System Sciences. 5 pp.–. https://doi.org/10.1109/HICSS.2000.926803[3] Peter M Allen, Rianne HJC Ravensbergen, Keziah Latham, Amy Rose, Joy Myint,
and David L Mann. 2018. Contrast sensitivity is a significant predictor of performance in rifle shooting for athletes with vision impairment. Frontiers in
psychology 9 (2018), 950.
[4] Shotaro Asano, Ryo Asaoka, Hiroshi Murata, Yohei Hashimoto, Atsuya Miki,
Kazuhiko Mori, Yoko Ikeda, Takashi Kanamoto, Junkichi Yamagami, and Kenji
Inoue. 2021. Predicting the central 10 degrees visual field in glaucoma by applying
a deep learning algorithm to optical coherence tomography images. Scientific
Reports 11, 1 (2021), 1–10.
[5] Halim Cagri Ates, Alexander Fiannaca, and Eelke Folmer. 2015. Immersive Simulation of Visual Impairments Using a Wearable See-through Display. In Proceedingsof the Ninth International Conference on Tangible, Embedded, and Embodied Interaction (Stanford, California, USA) (TEI ’15). Association for Computing Machinery,
New York, NY, USA, 225–228. https://doi.org/10.1145/2677199.2680551[6] Halim Cagri Ates, Alexander Fiannaca, and Eelke Folmer. 2015. Immersive simulation of visual impairments using a wearable see-through display. In Proceedings ofthe ninth international conference on tangible, embedded, and embodied interaction.
225–228.
[7] Mark S Baldwin, Sen H Hirano, Jennifer Mankoff, and Gillian R Hayes. 2019.
Design in the public square: Supporting assistive technology design throughpublic mixed-ability cooperation. Proceedings of the ACM on Human-Computer
Interaction 3, CSCW (2019), 1–22.
[8] Cynthia L Bennett and Daniela K Rosner. 2019. The Promise of Empathy: Design,
Disability, and Knowing the" Other". In Proceedings of the 2019 CHI conference onhuman factors in computing systems. 1–13.
[9] Puran S Bora, Zhiwei Hu, Tongalp H Tezel, Jeong-Hyeon Sohn, Shin Goo Kang,Jose MC Cruz, Nalini S Bora, Alan Garen, and Henry J Kaplan. 2003. Immunotherapy for choroidal neovascularization in a laser-induced mouse model simulating
exudative (wet) macular degeneration. Proceedings of the National Academy of
Sciences 100, 5 (2003), 2679–2684.
[10] Rupert Bourne, Jaimie D Steinmetz, Seth Flaxman, Paul Svitil Briant, Hugh R
Taylor, Serge Resnikoff, Robert James Casson, Amir Abdoli, Eman Abu-Gharbieh,
Ashkan Afshin, et al. 2021. Trends in prevalence of blindness and distance andnear vision impairment over 30 years: an analysis for the Global Burden of
Disease Study. The Lancet global health 9, 2 (2021), e130–e143.
[11] Kenny Tsu Wei Choo, Rajesh Krishna Balan, and Youngki Lee. 2019. Examining
augmented virtuality impairment simulation for mobile app accessibility design.
In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.
1–11.
[12] Hugo Chow-Wing-Bom, Tessa M Dekker, and Pete R Jones. 2020. The worse eyerevisited: Evaluating the impact of asymmetric peripheral vision loss on everyday
function. Vision research 169 (2020), 49–57.
[13] Tanvir Irfan Chowdhury, Sharif Mohammad Shahnewaz Ferdous, and John Quarles. 2017. Information recall in a virtual reality disability simulation. In ProceedingsSeeing our Blind Spots: Smart Glasses-based Simulation to Increase Design Students’ Awareness of Visual Impairment UIST ’22, October 29-November 2, 2022, Bend, OR, USAof the 23rd ACM symposium on virtual reality software and technology. 1–10.
[14] David P Crabb, Nicholas D Smith, Fiona C Glen, Robyn Burton, and David F
Garway-Heath. 2013. How does glaucoma look?: patient perception of visual
field loss. Ophthalmology 120, 6 (2013), 1120–1126.
[15] Michael Crabb, Michael Heron, Rhianne Jones, Mike Armstrong, Hayley Reid,and Amy Wilson. 2019. Developing Accessible Services: Understanding Current Knowledge and Areas for Future Support. In Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk)(CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12.
https://doi.org/10.1145/3290605.3300446[16] Carolyn Czoski-Murray, Jill Carlton, John Brazier, Tracey Young, Natalie L Papo,
and Hyong Kwon Kang. 2009. Valuing condition-specific health states using
simulation contact lenses. Value in Health 12, 5 (2009), 793–799.
[17] Roger Davis. 1994. (WEB)Vision Simulations. http://visionsimulations.com[18] Luce Drouet, Kerstin Bongard, Vincent Koenig, and Carine Lallemand. 2022.
Empathy in Design Scale: Development and Initial Insights. In Proceedings of the
Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing
Systems.
[19] Visual Impairment North East. 1984. Sim Specs. https://www.vinesimspecs.com[20] Augusto Esteves, Eduardo Velloso, Andreas Bulling, and Hans Gellersen. 2015.
Orbits: Gaze interaction for smart watches using smooth pursuit eye movements.
In Proceedings of the 28th annual ACM symposium on user interface software &technology. 457–466.
[21] Paul J Foster and Gordon J Johnson. 2001. Glaucoma in China: how big is the
problem? British journal of ophthalmology 85, 11 (2001), 1277–1282.
[22] Timothy R Fricke, Monica Jong, Kovin S Naidoo, Padmaja Sankaridurg, Thomas J
Naduvilath, Suit May Ho, Tien Yin Wong, and Serge Resnikoff. 2018. Global
prevalence of visual impairment associated with myopic macular degenerationand temporal trends from 2000 through 2050: systematic review, meta-analysis
and modelling. British Journal of Ophthalmology 102, 7 (2018), 855–862.
[23] Gabriel M. GAUTHIER, Gerard OBRECHT, Claude PEDRONO, Jean-Louis
VERCHER, and Lawrence STARK. 1987. ADAPTIVE OPTIMIZATION OF EYEHEAD COORDINATION WITH DEGRADED PERIPHERAL VISION (*). In Eye
Movements from Physiology to Cognition, J.K. O’REGAN and A. LEVY-SCHOEN(Eds.). Elsevier, Amsterdam, 201–210. https://doi.org/10.1016/B978-0-444-701138.50032-1[24] Florian Gekeler, Andre Messias, Max Ottinger, Karl Ulrich Bartz-Schmidt, andEberhart Zrenner. 2006. Phosphenes electrically evoked with DTL electrodes: astudy in patients with retinitis pigmentosa, glaucoma, and homonymous visual
field loss and normal subjects. Investigative ophthalmology & visual science 47, 11(2006), 4966–4974.
[25] Kathrin Maria Gerling, Regan L Mandryk, Max Valentin Birk, Matthew Miller,
and Rita Orji. 2014. The effects of embodied persuasive games on player attitudes
toward people using wheelchairs. In Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems. 3413–3422.
[26] Joy Goodman-Deane, Patrick M Langdon, P John Clarkson, Nicholas HM Caldwell,and Ahmed M Sarhan. 2007. Equipping designers by simulating the effects ofvisual and hearing impairments. In Proceedings of the 9th international ACM
SIGACCESS conference on computers and accessibility. 241–242.
[27] Joy Goodman-Deane, Sam Waller, Alice-Catherine Collins, and John Clarkson.
2013. Simulating vision loss: what levels of impairment are actually represented?.
In Contemporary Ergonomics and Human Factors 2013: Proceedings of the international conference on Ergonomics & Human Factors 2013, Cambridge, UK, 15-18
April 2013. CRC Press, 347.
[28] Joy Goodman-Deane, Sam Waller, Katie Cornish, and P John Clarkson. 2014. A
simple procedure for using vision impairment simulators to assess the visualclarity of product features. In International Conference on Universal Access in
Human-Computer Interaction. Springer, 43–53.
[29] Kunal Gupta, Gun A Lee, and Mark Billinghurst. 2016. Do you see what I see?
The effect of gaze tracking on task space remote collaboration. IEEE transactions
on visualization and computer graphics 22, 11 (2016), 2413–2422.
[30] Yuichi Hiroi, Yuta Itoh, Takumi Hamasaki, and Maki Sugimoto. 2017. Assisting
Eye Adaptation Via Occlusive Optical See-Through Head-Mounted Displays
AdaptiVisor.
[31] Chad Hochberg, Eugenio Maul, Emilie S Chan, Suzanne Van Landingham, Luigi
Ferrucci, David S Friedman, and Pradeep Y Ramulu. 2012. Association of vision
loss in glaucoma and age-related macular degeneration with IADL disability.
Investigative ophthalmology & visual science 53, 6 (2012), 3201–3206.
[32] Donald C Hood, Ali S Raza, Carlos Gustavo V de Moraes, Jeffrey G Odel, Vivienne C Greenstein, Jeffrey M Liebmann, and Robert Ritch. 2011. Initial arcuate
defects within the central 10 degrees in glaucoma. Investigative ophthalmology &visual science 52, 2 (2011), 940–946.
[33] Fork in the Road. 1994. Low Vision Simulators. https://www.lowvisionsimulators.
com[34] Braille Institute. 1994. Telephone Reader Program (TRP). https://brailleinstitute.
org/technology-for-vision-impaired/mobile-applications[35] Kodai Ito, Mitsunori Tada, Hiroyasu Ujike, and Keiichiro Hyodo. 2021. Effects of
the Weight and Balance of Head-Mounted Displays on Physical Load. Applied

Sciences 11, 15 (2021), 6802.
[36] Bei Jin, Zhuming Ai, and Mary Rasmussen. 2005. Simulation of Eye Disease in
Virtual Reality. Conference proceedings : ... Annual International Conference of theIEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine
and Biology Society. Conference 5 (02 2005), 5128–31. https://doi.org/10.1109/
IEMBS.2005.1615631[37] Bei Jin, Zhuming Ai, and Mary Rasmussen. 2006. Simulation of eye diseasein virtual reality. In 2005 IEEE engineering in medicine and biology 27th annual
conference. IEEE, 5128–5131.
[38] Pete R Jones, Tamás Somoskeöy, Hugo Chow-Wing-Bom, and David P Crabb.
2020. Seeing other perspectives: evaluating the use of virtual and augmented
reality to simulate visual impairments (OpenVisSim). NPJ digital medicine 3, 1
(2020), 1–9.
[39] Valerie Juniat, Rabia Bourkiza, Aditi Das, Raja Das-Bhaumik, Panagiota Founti,
Christopher Yeo, Rashmi Mathew, and Narciss Okhravi. 2019. Understanding
visual impairment and its impact on patients: A simulation-based training inundergraduate medical education. Journal of medical education and curriculardevelopment 6 (2019), 2382120519843854.
[40] Christoph M Kanzler, Jens Barth, Jochen Klucken, and Bjoern M Eskofier. 2016.
Inertial sensor based gait analysis discriminates subjects with and without visual
impairment caused by simulated macular degeneration. In 2016 38th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC). IEEE, 4979–4982.
[41] Moritz Kassner, William Patera, and Andreas Bulling. 2014. Pupil: an open
source platform for pervasive eye tracking and mobile gaze-based interaction.
In Proceedings of the 2014 ACM international joint conference on pervasive andubiquitous computing: Adjunct publication. 1151–1160.
[42] Claire Kearney-Volpe, Devorah Kletenik, Kate Sonka, Deborah Sturm, and Amy
Hurst. 2019. Evaluating instructor strategy and student learning through digitalaccessibility course enhancements. In The 21st International ACM SIGACCESS
Conference on Computers and Accessibility. 377–388.
[43] Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra, and Rajesh Krishna Balan. 2018. Empath-d: Vr-based empathetic app design for accessibility.
In Proceedings of the 16th Annual International Conference on Mobile Systems,
Applications, and Services. 123–135.
[44] Sharon Kingman. 2004. Glaucoma is second leading cause of blindness globally.
Bulletin of the World Health Organization 82 (2004), 887–888.
[45] Devorah Kletenik and Rachel F Adler. 2022. Let’s Play: Increasing Accessibility Awareness and Empathy Through Games. In Proceedings of the 53rd ACM
Technical Symposium on Computer Science Education V. 1. 182–188.
[46] Gregory Kramida. 2015. Resolving the vergence-accommodation conflict in headmounted displays. IEEE transactions on visualization and computer graphics 22, 7(2015), 1912–1931.
[47] Katharina Krösl, Dominik Bauer, Michael Schwärzler, Henry Fuchs, Georg Suter,and Michael Wimmer. 2018. A VR-based user study on the effects of vision
impairments on recognition distances of escape-route signs in buildings. The
Visual Computer 34, 6 (2018), 911–923.
[48] Katharina Krösl, Carmine Elvezio, Laura R Luidolt, Matthias Hürbe, Sonja Karst,
Steven Feiner, and Michael Wimmer. 2020. CatARact: Simulating cataracts in
augmented reality. In 2020 IEEE International Symposium on Mixed and Augmented
Reality (ISMAR). IEEE, 682–693.
[49] Kim Kullman. 2016. Prototyping bodies: A post-phenomenology of wearable
simulations. Design Studies 47 (2016), 73–90.
[50] Pupil Lab. 2014. (WEB)Pupil CORE. https://pupil-labs.com/products/core/techspecs/[51] Laurence S Lim, Paul Mitchell, Johanna M Seddon, Frank G Holz, and Tien Y
Wong. 2012. Age-related macular degeneration. The Lancet 379, 9827 (2012),
1728–1738.
[52] Chao Ma, Minoru Taya, and Chunye Xu. 2008. Smart sunglasses based onelectrochromic polymers. Polymer Engineering & Science 48, 11 (2008), 2224–
2228.
[53] Rhouri MacAlpine and David R Flatla. 2016. Real-time mobile personalized
simulations of impaired colour vision. In Proceedings of the 18th International
ACM SIGACCESS Conference on Computers and Accessibility. 181–189.
[54] J. Mankoff, H. Fait, and R. Juang. 2005. Evaluating accessibility by simulating theexperiences of users with vision or motor impairments. IBM Systems Journal 44,
3 (2005), 505–517. https://doi.org/10.1147/sj.443.0505[55] Paul Mitchell, Gerald Liew, Bamini Gopinath, and Tien Y Wong. 2018. Age-related
macular degeneration. The Lancet 392, 10153 (2018), 1147–1159.
[56] Richard Musil. 2022. HMD Geometry Database. https://risa2000.github.io/
hmdgdb/[57] Jun Nishida, Soichiro Matsuda, Mika Oki, Hikaru Takatori, Kosuke Sato, andKenji Suzuki. 2019. Egocentric Smaller-person Experience through a Change in
Visual Perspective. In Proceedings of the 2019 CHI Conference on Human Factors
in Computing Systems. 1–12.
[58] Jun Nishida and Kenji Suzuki. 2019. HYPERSPECTIVE: Shaping Experiences
beyond Perspectives. In 2019 IEEE Conference on Virtual Reality and 3D User
Interfaces (VR). IEEE, 1546–1549.

UIST ’22, October 29-November 2, 2022, Bend, OR, USA[59] World Health Organization et al. 2019. World report on vision. (2019).
[60] CG Owen, AE Fletcher, M Donoghue, and AR Rudnicka. 2003. How big is theburden of visual loss caused by age related macular degeneration in the United
Kingdom? British Journal of Ophthalmology 87, 3 (2003), 312–317.
[61] Produkt+Projekt. 1994. Aging Simulation Suit GERT. https://www.gtsimulators.
com/products/aging-simulation-suit-gert-ppd1002[62] Harry A Quigley. 1996. Number of people with glaucoma worldwide. British
journal of ophthalmology 80, 5 (1996), 389–393.
[63] S Rae, Keziah Latham, and Maria F Katsou. 2016. Meeting the UK driving vision
standards with reduced contrast sensitivity. Eye 30, 1 (2016), 89–94.
[64] Barry W. Rovner and Robin J. Casten. 2002. Activity Loss and Depression in
Age-Related Macular Degeneration. The American Journal of Geriatric Psychiatry
10, 3 (2002), 305–310. https://doi.org/10.1097/00019442-200205000-00010[65] Marilyn E Schneck, Anthony J Adams, Kenneth Huie, and Ellen Lee. 1993. A filter
for simulating color and spatial vision of the elderly. In Colour Vision Deficiencies
XI. Springer, 357–364.
[66] Ximing Shen, Yun Suen Pai, Dai Kiuchi, Kanoko Oishi, Kehan Bao, Tomomi
Aoki, and Kouta Minamizawa. 2021. Dementia Eyes: Perceiving Dementia with
Augmented Reality. In SIGGRAPH Asia 2021 XR (Tokyo, Japan) (SA ’21 XR).
Association for Computing Machinery, New York, NY, USA, Article 5, 2 pages.
https://doi.org/10.1145/3478514.3487617[67] Kristen Shinohara, Cynthia L Bennett, Wanda Pratt, and Jacob O Wobbrock. 2018.
Tenets for social accessibility: Towards humanizing disabled people in design.
ACM Transactions on Accessible Computing (TACCESS) 11, 1 (2018), 1–31.
[68] Barbara Sivak and Christine L. MacKenzie. 1990. Integration of visual informationand motor output in reaching and grasping: The contributions of peripheral andcentral vision. Neuropsychologia 28, 10 (1990), 1095–1116. https://doi.org/10.
1016/0028-3932(90)90143-C[69] Simon Stock, Christina Erler, and Wilhelm Stork. 2018. Realistic simulationof progressive vision diseases in virtual reality. In Proceedings of the 24th ACM
Symposium on Virtual Reality Software and Technology. 1–2.
[70] Kevin M Storer and Stacy M Branham. 2019. " That’s the Way Sighted People Do
It" What Blind Parents Can Teach Technology Designers About Co-Reading withChildren. In Proceedings of the 2019 on Designing Interactive Systems Conference.
385–398.
[71] Hans Strasburger, Ingo Rentschler, and Martin Jüttner. 2011. Peripheral visionand pattern recognition: A review. Journal of vision 11, 5 (2011), 13–13.

Zhang and et al.

[72] Ancret Szpak, Stefan Michalski, Dimitrios Saredakis, Celia Chen, and Tobias
Loetscher. 2019. Beyond Feeling Sick: The Visual and Cognitive Aftereffects ofVirtual Reality. IEEE Access PP (09 2019), 1–1. https://doi.org/10.1109/ACCESS.
2019.2940073[73] Deanna J Taylor, Laura A Edwards, Alison M Binns, and David P Crabb. 2018.
Seeing it differently: self-reported description of vision loss in dry age-related
macular degeneration. Ophthalmic and Physiological Optics 38, 1 (2018), 98–105.
[74] B Thylefors and AD2486713 Negrel. 1994. The global impact of glaucoma. Bulletinof the World Health Organization 72, 3 (1994), 323.
[75] Garreth W Tigwell. 2021. Nuanced perspectives toward disability simulations
from digital designers, blind, low vision, and color blind people. In Proceedings ofthe 2021 CHI Conference on Human Factors in Computing Systems. 1–15.
[76] Wikipedia. 2022. Comparison of virtual reality headsets, 2022-07-26 access. https://en.wikipedia.org/wiki/Comparison_of_virtual_reality_headsets[77] Joanne Wood, Alex Chaparro, Kaarin Anstey, Philippe Lacherez, Aaron Chidgey,
Jared Eisemann, Alison Gaynor, and Peter La. 2010. Simulated visual impairment
leads to cognitive slowing in older adults. Optometry and Vision Science 87, 12(2010), 1037–1043.
[78] Haojie Wu, Daniel H Ashmead, Haley Adams, and Bobby Bodenheimer. 2018.
Using virtual reality to assess the street crossing behavior of pedestrians withsimulated macular degeneration at a roundabout. Frontiers in ICT (2018), 27.
[79] Matteo Zallio, Sam Waller, Camelia Chivaran, and John Clarkson. 2021. Visual Accessibility and Inclusion. An Exploratory Study to Understand Visual
Accessibility in the Built Environment. (2021).
[80] Qing Zhang, Yifei Huang, George Chernyshov, Juling Li, Yun Suen Pai, and Kai
Kunze. 2022. GazeSync: Eye Movement Transfer Using an Optical Eye Tracker
and Monochrome Liquid Crystal Displays. In 27th International Conference onIntelligent User Interfaces. 54–57.
[81] Qing Zhang, Hiroo Yamamura, Holger Baldauf, Dingding Zheng, Kanyu Chen,
Junichi Yamaoka, and Kai Kunze. 2021. Tunnel Vision–Dynamic Peripheral Vision
Blocking Glasses for Reducing Motion Sickness Symptoms. In 2021 International
Symposium on Wearable Computers. 48–52.
[82] Xucong Zhang, Yusuke Sugano, Mario Fritz, and Andreas Bulling. 2017. Mpiigaze:
Real-world dataset and deep appearance-based gaze estimation. IEEE transactions
on pattern analysis and machine intelligence 41, 1 (2017), 162–175.
[83] George J. Zimmerman. 1979. Zimmerman Low Vision Simulation Kit. http://www.lowvisionsimulationkit.com