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Using an Augmented Reality Device as a Distance-based Vision Aid—Promise and Limitations

Kinateder, Max PhD1*; Gualtieri, Justin2; Dunn, Matt J. PhD, MCOptom3; Jarosz, Wojciech PhD2; Yang, Xing-Dong PhD2; Cooper, Emily A. PhD1,2

Author Information
doi: 10.1097/OPX.0000000000001232
  • Open


For the millions of people who are affected by low vision and blindness, independence and mobility can pose daily challenges.1–3 To address these challenges and improve the functional vision of this population, a range of assistive tools have been developed, including vision aids and sensory substitution devices. Recently, available tools have included custom head-mounted display systems designed to digitally enhance visual information, such as Jordy (Enhanced Vision, Huntington Beach, CA), LVES,4 eSight (eSight, Toronto, Ontario, Canada), and NuEyes (NuEyes, Newport Beach, CA). The basic principle of these head-mounted displays is to substitute the image cast by the world on the retina with an enhanced view. Outward-facing cameras capture live video of the world in front of the user; this video is processed to increase visibility via magnification or contrast enhancement and then shown in (near) real time to the user through a pair of microdisplays positioned in front of the eyes.5–7 This is called a “video see-through display” because although the system is mobile, the users' eyes are covered by opaque screens. While these systems are promising and can measurably increase functional vision,6 they also tend to suffer from temporal lag, cumbersome hardware, and reduced visual field. To date, no video see-through system has been widely adopted.

At the same time, head-mounted displays have emerged as a popular platform for mass consumer electronics, with a range of companies selling these systems to general consumers for virtual and augmented reality applications. In particular, optical see-through augmented reality systems, such as Glass (Google, Mountain View, CA) and HoloLens (Microsoft, Redmond, WA), can augment vision without having to cover the eyes with an opaque screen. These commercial products also benefit from the cost savings of mass production, improvements in form factor, and the ability to flexibly support a range of software applications. Despite the lower contrast typical of see-through displays, these augmented reality systems have several potential advantages compared with video see-through displays. For example, the user's natural field of view is intact, and their eyes are unoccluded. Thus, the incorporation of assistive features into a consumer augmented reality system provides a potential new avenue for broadening the impact of this technology on the low vision and blind population, much like consumer smartphones have broadened the availability of handheld assistive tools.8

One early study used off-the-shelf head-mounted displays to build a see-through visual multiplexing device for visual field loss,9 but at the time additional custom hardware was required to achieve the desired effect. A more recent study examined visual acuity and sensitivity for text and shapes presented on a see-through augmented reality system, showing that a variety of virtual content can be visible to visually impaired users on a consumer system.10 However, no specific assistive applications were explored. Another recent study showed that overlaying enhanced edge information on a see-through head-mounted display can increase contrast sensitivity in simulated visual impairment.11 Here, we build on this prior work to examine alternative avenues for visual enhancement using consumer augmented reality.

The question of how best to augment visual information is still an open one,5,12–14 particularly for individuals with near-complete vision loss (i.e., individuals with severely impaired vision or legal blindness).15 Selectively enhancing edges that indicate object boundaries may simplify complex visual patterns so as to help with parsing natural scenes when visual information is limited.16–20 In particular, a few previous studies have used a “distance-based” enhancement system that translates the distance of points in front of the user into pixel brightness values and showed that visually impaired users wearing this video system could perform a visual search task while seated16 and collided with fewer obstacles in a mobility task.21 A similar approach was recently implemented in a custom-built see-through system.22 Here, we examine the ability of emerging consumer augmented reality hardware (Fig. 1) to implement a similar distance-based visual augmentation, with a focus on usability for individuals with near-complete vision loss. We focus on this group specifically because prior work and our own pilot testing suggest that they may be the most likely to find utility in distance-based information. Thus, we test the hypothesis that distance-based augmented reality can improve functional vision in this target population for a range of tasks. We develop an application to run on the HoloLens that translates spatial information from the physical environment into an augmented reality view containing simplified patterns with high-contrast edges between objects at different distances. We then examine the impact of the application on performance of a range of visual tasks in an exploratory study with visually impaired users with a range of etiologies (n = 4) and in a study using a larger sample of people with simulated visual impairment (n = 48). We focus on understanding existing strengths, areas of potential, and current limitations.

HoloLens hardware. (A) The HoloLens has binocular see-through displays (red arrows), a sensor bar (blue dashed box), and an onboard computer. (B) Users wear HoloLens by tightening an adjustable band around the head and positioning the screen in front of their eyes.



The HoloLens is a head-mounted augmented reality system that can display three-dimensional (3D) virtual surfaces within the physical environment.23 The system includes two see-through displays that subtend approximately 30° horizontally and 17.5° vertically in each eye (Fig. 1A, red arrows). A set of sensors (Fig. 1A, blue dashed box)–including four scene-tracking cameras, an infrared-based depth sensor, and an inertial measurement unit–continuously tracks the user's position and orientation in the environment. As the user moves around, the HoloLens also measures and stores the dimensions and shape of the physical space around them, creating a 3D reconstruction of the surrounding environment. This 3D reconstruction is provided to developers as a triangle mesh, in which the number of individual triangles used to define the environment per unit area determines the resolution and detail of the 3D map. User input is accepted via multiple channels, including speech, hand gestures, and Bluetooth devices. All computation is completed on board, so the system is untethered (Fig. 1B) and has a battery life of two to three hours with active use. It weighs approximately 580 g.

Application Development

Software development was performed using Microsoft's HoloToolkit and Unity (Unity Technologies, San Francisco, CA). We developed an application that measures the distance of surfaces and objects in the environment from the user by accessing the user's position and the 3D environment map. The application discretizes these distances into a set of bands, each with a unique color and intensity value. The bands are directly overlaid semitransparently on the environment in stereoscopic 3D when viewed through the displays (Figs. 2A, B), creating an augmented reality environment that is a mixture of real and virtual surfaces. The augmented reality environment has a simplified visual geometry, with high-contrast edges between objects and surfaces at different distances from the observer, which we hypothesize is more easily interpretable with impaired vision relative to the original view.16,17,19 When using the system, the natural field of view is unrestricted, so the appearance is similar to having a window into the augmented reality environment through the HoloLens display (see above). As the user moves around the environment, the colors change to reflect the distances from the current viewpoint. The mapping between distance and color is arbitrary. We created 18 unique mappings to enable customization for different levels of visual impairment and color vision (Fig. 2C shows nine examples). Some mappings transition between two colors from high to low saturation (left column), some transition from white to one color (middle column), and some transition from high to low opacity (right column). Because the HoloLens displays can produce light but cannot occlude it, transitions from white to black are not possible. In addition, the overall luminance and opacity of the overlays are adjustable, which is useful for cases in which a user is particularly light sensitive or for transitioning between environments with differing ambient light levels. The source code for our application is freely available for research purposes.

Augmented reality application. (A, B) The HoloLens creates a 3D map of the physical environment (A) and can overlay an augmented stereoscopic view (B). The example overlay shows nearby surfaces (<2.0 m) as warm colors, and farther surfaces as cool colors (2.0 m and farther). This is the red-to-blue augmented reality (AR) used in Experiment 2. (C) Several other example views of the same scene demonstrate how the color and intensity can be customized for individual users. These is a subset of the options presented to participants in Experiment 1. All examples were captured from the HoloLens using the scene camera positioned between the user's eyes.

In Experiment 1, we allowed users to select any one of the 18 mappings that created the most visible contrast between the foreground and background of a scene. In Experiment 2, we used two different mappings (red to blue [shown in Fig. 2B] and high to low opacity). In both experiments, the update rate for the display and motion tracking was set to 60 Hz, and the resolution of the 3D environment mesh was set to the highest density that produced noticeable improvements in 3D detail (~2000 triangles per cubic meter). There was a 1-second delay between subsequent mesh updates, which was necessary for the system to scan and process the updated mesh. Thus, all visual identification tasks were performed with the target person, object, or gesture held stationary. Because of the fast tracking of user-generated motion, there was no noticeable lag associated with body or head movements.

The number of discrete color bands was set to 10, and distances closer than 0.5 m were not augmented, so as not to impede near vision. In Experiment 1, the first band covered 0.5 to 1.5 m, the eight middle bands were each 0.25 m wide, and the final band covered distances beyond 3.5 m. In Experiment 2, the first band extended to only 1.0 m, and all other bands were also moved closer by 0.5 m accordingly.


All participants in both experiments gave written informed consent and were compensated. The procedures were approved by the Dartmouth College Institutional Review Board and comply with the Declaration of Helsinki. The procedures and main hypotheses of Experiment 2 were pre-registered on (#2870). For clarity, Table 1 provides a summary of the participants, tasks, and number of trials conducted in each experiment.

Summary of experiments and tasks

Experiment 1: Exploratory Study with Visually Impaired Participants


Four participants were recruited via an e-mail advertisement. Table 2 provides individual information about each participant. Note that participant 4 works as a professional accessibility services manager. Participants were recruited with a range of conditions causing generalized vision loss and in some cases visual field restriction.

Information about participants and their self-reported level of vision in Experiment 1

The experimenter calibrated the HoloLens for each participant in a two-step procedure. First, all pixels were turned on uniformly, and the device was adjusted to make sure that the displays were visible, and the overall brightness was at a comfortable level. Next, the experimenter stood 1.5 m from the participant and turned on an initial color setting. The participant looked around and determined whether they could visually identify the location and shape of the experimenter's body. At this stage, each participant indicated that they could see the experimenter. We then interactively determined the color setting that created the strongest perceived contrast between foreground and background. Finally, the experimenter stepped slowly backward to confirm that the visible contrast changed with distance. Of the four participants, one selected red-to-blue (participant 1), two selected yellow-to-blue (participants 2 and 3), and one selected white-to-blue (participant 4).


We conducted four naturalistic tasks, each consisting of two blocks of five trials. The first block was completed with the augmented reality turned off (baseline), and the second block with the augmented reality on, and the trial order within each block was pseudorandomized. Participants' performance (correct/incorrect) and confidence (from 1 “it's a guess” to 3 “very certain”) were recorded for each trial. Tasks were selected to represent different levels of difficulty in visual identification, as well as mobility. Participants performed all tasks in an indoor space with typical overhead lighting. Prior to starting each task, participants performed a brief practice both with and without visual augmentation.

Person localization: Participants sat in a chair, and a life-size cutout figure of a person was placed 1.8 m away from them. The location of the figure was pseudorandomly assigned to one of five positions (−45.0, −22.5, 0.0, 22.5, and 45.0° from “straight ahead”). On each trial, the participants indicated the location of the figure using a laser pointer. The experimenter scored hits (1), near misses (0.5, the laser pointer missed the cutout figure only slightly), and misses (0). After each trial, participants rated their confidence.

Pose recognition: On each trial, the experimenter stood 1.5 m from participants and held their his/her in one of five different poses (arms straight out to the side, arms up forming a “Y,” arms above the head forming an “O,” one arm straight up/one arm straight down, one arm bent down at elbow/one arm bent up at elbow). The experimenter wore a black long-sleeved jacket, and the wall behind them was beige with some decorations, so that there was high contrast between the foreground and background even without any augmentation. Participants mirrored each pose with their arms and indicated their confidence. The response was recorded with a photograph and later scored by a naive judge on a 3-point scale, with 0 indicating incorrect, 0.5 partially correct, and 1 fully correct.

Object recognition: Participants identified objects that were placed one at a time on a table 1.5 m in front of them and reported their confidence. The objects were a spray bottle, table lamp, square wicker basket, recycling bin, and fake plant (Fig. 3A). Prior to starting the task, participants were given time to touch and look at each of the objects and identify them verbally. To control for memory effects, the experimenter read aloud the list of objects before each block. Participant responses were scored as either incorrect or correct.

Example tasks. (A) Images of the five objects. To decrease the probability of getting the correct answer based purely on the approximate size of the object, several objects were selected to have a similar shape and size. (B) Example of one of the three unique room layouts used in the mobility task for Experiment 2. Layouts were composed of a set of tables and chairs in different locations, with different objects placed on the tables as well.

Mobility: Participants walked forward from a fixed location and stopped when they identified an obstacle in their path (a white portable room divider 1.7 × 1.6 m). All participants except participant 4 completed the task without a cane. In each trial, the obstacle was placed at a pseudorandomly selected location between 5.5 and 7.5 m from the starting position. After participants stopped, the experimenter measured the distance between them and the obstacle using a laser range-finder. Confidence scores were not collected, because participants were instructed to stop as soon as they detected the obstacle.

Experiment 2: Controlled Experiment with Simulated Vision Loss


Forty-eight participants (mean age, 21.15 years; 34 female participants) were recruited, all with normal or corrected-to-normal visual acuity (0.00 logMAR or better) and normal stereoacuity (70 arcsec or better) assessed with a Randot Stereo Test (Precision Vision, LaSalle, IL). During all tasks, participants wore a pair of swim goggles modified binocularly with Bangerter occlusion foils (type LP; Ryser Optik, St. Gallen, Switzerland), which degrade visual acuity uniformly across the visual field.24 The LP-type foils simulate visual acuity at the level of perceiving hand movements, with some rough shapes and forms distinguishable under typical indoor lighting. For each participant, we verified that the simulators resulted in letter acuity less than 1.60 logMAR (approximately 20/800), inability to count fingers at 1.0 m, and intact perception of hand movements. One session was repeated because of technical errors.


Participants were randomly assigned to one of three groups (n = 16). In the color group, the red-to-blue augmented reality color mapping was used (Fig. 2B). In the opacity group, the bands had differing levels of opacity: near distances were most opaque, and distances beyond the ninth band were fully transparent. In the control group, participants were told that the HoloLens would augment their vision; however, no actual augmentation was displayed (at the start of each task for which vision was supposed to be augmented, the HoloLens screen flashed blue and faded back to being fully transparent). This group was included to examine potential practice effects or increases in effort/attention associated with the knowledge of augmented vision.

Visual identification tasks

Participants performed three identification tasks, each consisting of two blocks of six trials (the first block with the augmented reality turned off and the second block with the augmented reality on). The overall procedure used was the same as the exploratory study, but the study was carried out in a different location and with some differences in the tasks. Three naive judges scored pose and gesture recognition accuracy, and their ratings were averaged to determine the final score.

Pose and object recognition: These tasks were performed in the same manner as described in Experiment 1, with the exception that the viewing distance for poses was 2.2 m. A sixth pose (“both arms straight up”) and object (stack of books) were also included. The interrater reliability of the scoring for poses was 0.78 (Fleiss κ), suggesting substantial agreement (defined as 0.61 to 0.80).25

Gesture recognition: To assess the spatial resolution of the augmented vision, the experimenter stood 1.2 m from the participants and made one of six gestures with their right hand held to their side (thumb-up, shaka [“hang loose”], open palm, fist, peace sign, okay). The participants mirrored the hand gesture and indicated their confidence. Responses were scored as for the poses, and interrater reliability was 0.63.

Mobility task

Participants explored a room (5.3 × 3.6 m) with an unknown layout in three trials. On each trial, the furniture in the room was arranged in one of three different layouts (selected pseudorandomly), and the participants were given 60 seconds to complete the task (Fig. 3B). On the first trial, the augmented reality remained off (baseline). There were two test trials: one in which the augmented reality was on and another in which a white cane was used as an assistive tool. The ordering of these two trials was determined pseudorandomly. Prior to the cane trial, participants practiced using the cane in a different room. After each trial, participants rated their level of agreement on a scale of 1 (strongly disagree) to 7 (strongly agree) with four statements: “Overall, I felt comfortable while exploring the room,” “I felt unlikely to run into things,” “It was easy to navigate the space,” and “I felt that my vision provided useful information.” After all trials, participants indicated whether baseline, augmented reality, or cane was the best with respect to each of these statements. Because we used the same room with different layouts, the HoloLens' storage of overlapping spatial meshes could cause technical issues. Thus, between trials, we cleared the system memory and circled the room once to orient the system to the new layout (note that this problem does not occur if the system is moved to a new room).

Data Analysis

All data were analyzed using the R Environment for Statistical Computing, version For Experiment 1, in some cases, participants were unable to detect any visual information during the baseline trials and did not provide guesses. On these trials, confidence was scored as zero (note that this was the case for all baseline trials for participant 4). For Experiment 2, effects of the independent variables (experiment group [control/color/opacity] between subjects and trial block [baseline/augmented reality] within subjects) were assessed using repeated-measures ANOVAs (significance level of P < .05). For post hoc analyses, P values were Bonferroni corrected. Normality of data from Experiment 2 was tested using Shapiro-Wilk tests. For gesture and pose recognition in Experiment 2, analyses were performed on the average accuracy ratings of the three judges. Because of technical errors, data from one trial in Experiment 1 and one trial in Experiment 2 were not recorded. The raw response data and analysis code are provided on publicly accessible repositories.


Experiment 1

Accuracy and average confidence ratings for each of the four participants in the visual identification tasks are shown in Figs. 4A to C. Each pair of colored bars shows the results for an individual participant's baseline and augmented reality trials. Participants 1, 2, and 3 were able to complete the person localization task consistently both with and without augmented reality and reported high confidence (Fig. 4A). Participant 3 (brown bars) indicated after the task that the augmentation made her more confident (despite her ratings being similar). However, participant 1 (magenta bars) remarked that the checkered shirt of one experimenter was actually more visible without the augmentation. Participant 4 (yellow bars) was unable to locate the figure without augmented reality, but correctly located it on 80% of trials with augmented reality, with medium confidence. Similar patterns were observed for pose recognition (Fig. 4B). Participants 1 and 2 performed the task with high accuracy and confidence, but for this task participant 3 had lower accuracy overall (compared with person localization) and reported higher confidence with augmented reality. Participant 4 was unable to perform the task at baseline, but obtained reasonably accurate performance (with low confidence) with augmented reality. Qualitatively, all but participant 2 improved in object recognition in the augmented reality block (Fig. 4C), whereas participant 2 decreased slightly both in performance and confidence.

Experiment 1 results. Results are shown for person localization (A), pose recognition (B), object recognition (C), and mobility (D) tasks. Bar heights in A to C indicate percent correct (left column) and average confidence (right column) of each participant across baseline and augmented reality (AR) trial blocks. Bar heights in D indicate mean distance each participant stopped in front of the obstacle in the two trial blocks. On the last AR trial, participant 4 changed direction and walked toward a wall. He detected the wall visually using the AR color before hitting it with his cane, so this distance was recorded and used for analysis. Error bars in confidence ratings and mobility task indicate standard error. AR = augmented reality; p1–4 = participants 1 to 4.

The results for the mobility task are shown in Fig. 4D, in terms of the average distance each participant required to detect the obstacle and stop walking. In most trials without augmented reality, participants 1, 2, and 3 detected the obstacle one or two steps before reaching it. Participant 1 detected the obstacle on average at a similar distance in the baseline and augmented reality blocks (1.4 and 1.6 m). However, he reported using a different strategy in the two conditions: in the baseline trials, he used the contrast between the obstacle and the background; when using augmented reality, he instead relied on the color-distance information. This participant also indicated that the augmentation worked well for him to identify walls and used it to guide himself to stop each time he returned to the starting position. Participants 2 and 3 both tended to detect the obstacle in the augmented reality block from approximately 3 m, which roughly matches the onset distance of the farthest color transition; however, participant 3 indicated that using a cane would be simpler. Participant 2 walked fastest and on some trials experienced issues with the color map not updating quickly enough. Participant 4 could not detect the obstacle visually at baseline, so he used his cane. In one baseline trial and one augmented reality trial, the participant changed direction prior to reaching the obstacle and thus never located it. However, on each of the other augmented reality trials, he detected the obstacle visually before hitting it with his cane, with an average distance of 1.88 m.

Other Responses

Participants also reported on the strengths and weaknesses of the application and the hardware after completing all tasks. Participant 1 stated that if the hardware had the same form factor as a pair of glasses it would be useful and that providing distance information relative to the head was preferable for him than receiving this feedback on other parts of the body (like the arm). Overall, he said the system was somewhere between distracting and helpful. Participant 2 stated that overall his vision was worse with the HoloLens and that the lag time was a problem (as we observed during the mobility task). Participant 3 also expressed that the current form factor of the system was undesirable but that she might find the system particularly helpful at night. Participant 4, whose vision was more strongly impaired than the others' and most improved when using the augmented reality system, noted that he had to move his head around more in the identification tasks. This may reflect the limited display size in the visual field. However, unlike the other participants, he indicated that the device was comfortable as is and that the form factor was not an issue.

Overall, these results suggest that improvements in functional vision (particularly for object identification and obstacle detection during mobility) may be achievable with the augmented reality system but indicate that the usefulness of the distance-based augmentation likely varies by task and visual ability. In addition, these results on their own do not rule out the possibility that any objective or subjective changes in vision could be due to increased attention, effort, or practice during the trials with augmented vision.

Experiment 2

In this study, we examined the potential changes in functional vision created by the augmented reality system in a larger sample of participants with simulated vision loss. We also examined the potential impact of the system novelty on our measures of performance by inclusion of a control group.

Visual Identification Tasks

The results from each of the three visual identification tasks for mean accuracy (top row) and confidence (bottom row) are shown in Fig. 5, separately for the control group (gray bars), color group, and opacity group (orange bars). Recall that the procedure for the baseline blocks (light shaded bars) was identical for each group, so variability across groups can be attributed to random variance, and that the control group was told they would have augmented vision, but after a brief flash the HoloLens display was actually turned off. The three tasks were selected to range from easy (pose recognition) to difficult (object and gesture recognition) when performed at baseline. This is reflected by the fact that baseline accuracy and confidence are overall high for pose recognition and relatively low for object and gesture recognition. A useful vision aid should ideally improve performance on tasks that are challenging, but importantly, it should also not degrade performance on tasks that are already easily accomplished with unaugmented vision.

Experiment 2 results for visual identification tasks in terms of percent correct (A–C) and confidence ratings (D–F). Bar heights indicate the mean across participants within each group (control, color, opacity), and error bars indicate standard error. Results for each group are summarized with two bars that represent data from the first (baseline) and second (AR) block of trials. AR = augmented reality. ***P < .001; *P < .05.

First, we examined the effect of the augmentation on accuracy in each task. For pose recognition (Fig. 5A), there were no significant main effects or interaction terms for experimental group (control/color/opacity) or trial block (baseline/augmented reality) variables (experiment group: F2,45 = 0.72, P = .49, ηp2 = 0.03; trial block: F1,45 = 0.82, P = .37, ηp2 = 0.02; interaction: F2,45 = 0.15, P = .86, ηp2 = 0.01). Thus, although performance did not significantly improve with augmented reality on this task, it also did not get worse. This is not entirely surprising, because performance was already quite high at baseline because of the high visual contrast (average percent correct across all groups was 61.3%). For object recognition (Fig. 5B), significant main effects of experiment group and trial block were mediated by a significant interaction term (F2,45 = 13.01, P < .001, ηp2 = 0.37). Post hoc comparisons showed that performance improved significantly during the augmented reality block in the color and opacity groups, but not in the control group (control: t95 = 0.18, P = .86, d = 0.03; color: t95 = 7.59, Pcorrected < .001, d = 1.10; opacity: t95 = 7.36, Pcorrected < .001, d = 1.06). Similarly, there was a significant interaction term (F2,45 = 3.66, P < .05, ηp2 = 0.14) in the gesture recognition task (Fig. 5C), reflecting the fact that participants in the opacity group performed better in the augmented reality block (t95 = 2.88, Pcorrected < .05, d = 0.42). This suggests that participants were able to use the information provided by the augmented vision to more accurately perceive the shape of the objects and the form of a hand gesture. In the case of gestures, the improvement was minor and likely not of practical use. For objects, however, this improvement was substantial; the average percent correct was 65.0% using augmented reality as compared with 19.4% without (over six trials). This is a promising amount of improvement, particularly considering that the level of simulated visual impairment was severe. The ability to reliably recognize everyday objects visually with this system thus represents a practical improvement in functional vision.

Similar to the accuracy results, confidence ratings showed that participants overall rated their confidence to be highest in the pose recognition task and lowest in the object and gesture tasks. The confidence ratings for poses are shown in Fig. 5D. As with accuracy, there were no significant main effects or interaction terms (experiment group: F2,45 = 0.64, P = .53, ηp2 = 0.03; trial block: F1,45 = 1.32, P = .26, ηp2 = 0.03; interaction: F2,45 = 1.06, P = .35, ηp2 = 0.05). For object recognition (Fig. 5E), however, significant main effects were again mediated by a significant interaction term (F2,45 = 5.55, P < .01, ηp2 = 0.20). Participants reported higher confidence during the augmented reality block in both the color and the opacity groups, but not in the control group (control: t95 = 2.01, Pcorrected = .19, d = 0.29; color: t95 = 6.75, Pcorrected < .001, d = 0.97; opacity: t95 = 8.30, Pcorrected < .001, d = 1.20). Finally, confidence ratings for gesture recognition (Fig. 5F) also showed a significant interaction term (F2,45 = 3.48, P < .05, ηp2 = 0.13), reflecting higher confidence in the augmented reality block in the color and opacity groups (color: t95 = 4.94, Pcorrected < .001, d = 0.71; opacity: t95 = 5.39, Pcorrected < .001, d = 0.78).

These results show that participants tended to be more confident in the two more difficult tasks when using the augmented reality system. This makes sense for object recognition, in which their performance improved with augmented reality. The confidence that a user has with his/her augmented vision likely plays a key role in how willing he/she is to rely on visual information and perform tasks independently. It is interesting that confidence increased for gesture recognition as well, because performance was only modestly impacted. In the next section, we report an exploratory analysis assessing the possibility that using augmented vision might produce overconfidence: an increase in confidence even when perceptual judgments are incorrect. In this and subsequent analyses, we combined the two test groups (color and opacity are grouped together), because the pattern of results was highly similar.

Confidence as a Function of Performance

In all visual identification tasks, confidence ratings and performance were significantly positively correlated (poses: r = 0.53, P < .001; objects: r = 0.43, P < .001; gestures: r = 0.17, P < .001). Fig. 6 shows the average confidence ratings for each task separately for trials in which participants gave correct or incorrect responses (for pose and gesture recognition, trials with a score >0.75 were categorized as “correct,” trials with scores <0.25 were categorized as “incorrect”). Across all tasks, experiment groups, and trial blocks, participants tended to report higher confidence in trials in which they gave correct answers. Interestingly, partially overlapping t tests (Bonferroni corrected for 12 comparisons; note that the number of observations in each bin varied) revealed that participants in the test groups reported higher confidence in the augmented reality block, even when they gave incorrect answers.27 The only exception is the incorrect trials for pose recognition (Fig. 6A). This overconfidence was not observed in the control group. This underscores the importance of considering how to provide feedback and training to help users understand how reliable their vision is when they use an unfamiliar assistive device.

Relationship between performance and confidence. Mean confidence ratings are shown separately for correct/incorrect trials in pose (A), object (B), and gesture (C) recognition. Data are plotted as in Figure 4, except the two test conditions (color and opacity) are combined. AR = augmented reality. Partially overlapping t tests were used to compare means between binned data. t Statistics for significant differences: pose recognition, correct trials, test condition: t 121.7 = 3.08; object recognition, correct trials, test condition: t 112.15 = 4.81; Object recognition, incorrect trials, test condition: t 144.26 = 5.49; gesture recognition, correct trials, test condition (unpaired t test): t 17 = 3.69; gesture recognition, incorrect trials, test condition: t 144.26 = −5.49. ***P < .001; **P < .01, *P < .05.

Mobility Task

Fig. 7 shows the results from the participants' responses after the mobility task. Rather than detect a single obstacle, in this task participants were given time to freely explore an unfamiliar room. For simplicity, responses are plotted as difference scores by subtracting out each participant's response in the baseline trial. Overall, these results show that reported improvements were similar across both the control and test groups, suggesting that the subjective assessments used in this task did not measure any potential effects of the augmented reality system on mobility. In both the control and test groups, participants tended to report feeling less likely to collide with obstacles when using a cane and when using augmented reality (Fig. 7A). An ANOVA revealed only a main effect of trial type (trial type [baseline/cane/augmented reality]: F2,92 = 22.72, P < .001, ηp2 = 0.33; experiment group [control/test]: F1,46 = 0.02, P = .89, ηp2 < 0.01; interaction: F2,92 = 0.80, P = .45, ηp2 = 0.02), and differences relative to baseline were statistically significant for all conditions except when the control group used the cane (test/augmented reality: t31 = 3.45, Pcorrected < .01; test/cane: t31 = 6.01, Pcorrected < .001; control/augmented reality: t15 = 3.76, Pcorrected < .01; control/cane: t15 = 2.31, Pcorrected = .14). When comparing collision risk, 65.5% of the test group preferred the cane, and 34.5% preferred augmented reality. In the control group, 56% and 38% preferred the cane and augmented reality, respectively. Similarly, participants in both groups tended to report that their vision was more useful with augmented reality (Fig. 7B). There was also a main effect of trial type on these responses (trial type: F2,92 = 13.14, P < .001, ηp2 = 0.22; experiment group: F1,46 = 0.10, P = .76, ηp2 < 0.01; interaction: F2,92 = 0.45, P = .64, ηp2 = 0.01), which reflected a statistically significant increase in both groups when using augmented reality (control: t15 = 3.65, Pcorrected < .01; test: t31 = 0.10, Pcorrected < .01). When comparing usefulness of vision, 78.1% of the test group and 62.5% in the control group reported that augmented reality was preferred. Because the control group experienced no real augmentation, these results together indicate that these subjective ratings are likely an unreliable measure of mobility improvements in augmented reality. For the two other statements (“Overall I felt comfortable while exploring the room,” “It was easy to navigate the space”), no significant effects of using a cane or augmented reality were found.

Experiment 2 mobility task results. Differences in subjective responses compared with baseline in the control group and test groups are shown for risk of collision (A) and usefulness of vision (B). Positive values indicate ratings higher than baseline, and the maximum absolute difference is 6. Error bars indicate standard error. AR = augmented reality. ***P < .001; **P < .01.


The advent of mass-market consumer augmented reality systems, together with the rapid development of assistive mobile technology, holds substantial promise for providing tools to visually impaired individuals. Although the diversification and increased availability of high-tech tools might assist in performing many day-to-day tasks, the precise potential benefits and challenges are still unclear. Here, we presented two experiments using an application developed and deployed on a consumer augmented reality device, which provides high-contrast, customizable distance information overlaid in the user's field of view. The results suggest areas in which current augmented reality systems may be used to improve functional vision and where they fall short.

Overall, our findings support previous work suggesting that simplifying visual scenes can be helpful for people with severely impaired vision and show that this approach can be implemented in a see-through head-mounted display.16,17,19,21 However, our studies indicate that the utility of the current system varies substantially as a function of task. Experiment 1 also suggests that this particular system may not be desirable in all forms of vision loss, both because visual detail from surface texture can be lost and because the resolution of the HoloLens 3D spatial mesh is limited. This does not preclude the potential utility of augmented reality for these users, who may instead find benefits from overall edge or contrast enhancement.11 The flexibility of consumer devices provides a potential platform to create a variety of applications from which a selection can be made depending on a user's level of visual ability. However, the type of applications that are possible and how they should differ for different users are an area that requires further research. Although low vision and blindness simulators are frequently used to examine task performance in controlled settings,11,28,29 future work should examine systematically how the acuity levels and visual field loss associated with specific etiologies may be supplemented with augmented reality.

Major limitations of the current HoloLens system include the fact that it updates distance information at only up to 1 Hz, so visual perception of fast-moving objects may be degraded. However, the display can provide low-latency self-motion information because it builds up a stable 3D map as the user moves around a stationary environment. Nonetheless, the lag and limited range of the mapping are clear limitations of the device, which will hopefully improve with the next generations of head-mounted displays. As 3D sensing technologies improve, the ability to quickly update both self and environmental motion will be essential. At the same time, the portion of the visual field covered by the see-through display of the HoloLens is quite limited (30° horizontally). Key information for several activities, such as navigation, may often fall in the peripheral visual field, so improvements in the display size are highly desirable. In addition, the distance-based nature of the current system means that regions of high visual contrast but low depth variance would likely be degraded visually. Future generation systems could detect object boundaries using a combination of depth and image-based measures.30 In this case, it may be possible to dynamically adjust the pattern or opacity of overlaid depth information to minimize interference with other visual details. Finally, in its current state, the display brightness is limited and best suited for indoor environments.

Our results also suggest an interesting effect of augmented reality on visual confidence. Visual confidence (i.e., an observer's ability to estimate the reliability of their own perception31) might be of particular importance for users who adopt head-mounted display-based tools. While people have extensive experience with which to estimate the reliability of their unaided vision, they have no immediate access to quantitative diagnostics of a head-mounted display. As with other assistive devices, training, practice, or calibration is likely to be necessary in order for users to learn the correct level of visual confidence. Here, we found that accuracy was indeed positively correlated with confidence. However, we also found that when participants used augmented vision, their visual confidence was higher compared with baseline, even when they gave incorrect answers. However, it is important to note that this observation was made from a sample of participants with simulated, temporary visual impairments and thus may not generalize to other populations. Future research will therefore need to expand our understanding of visual confidence in augmented reality.

Based on the results and feedback in these studies, several future directions are conceivable. For instance, recent advantages in computer vision could be harnessed to develop “smart” overlays that are able to highlight flat and uneven surfaces and identify stairs, apertures, or even people. In addition, more sophisticated algorithms to automatically provide enhanced spatial information could potentially be implemented in real-time augmented reality.32,33 The rapid developments in mobile consumer devices' computing power together with universal platforms for application development provide increasing opportunities to broaden and improve visual assistive technology.


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Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Optometry.