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    Michele Fiorentino*

    Saverio Debernardis

    Antonio E. Uva

    Giuseppe Monno

    Dipartimento di Meccanica,

    Matematica e Management

    (DMMM)

    Politecnico di Bari

    70126 Bari, Italy

    Augmented Reality Text StyleReadability with See-ThroughHead-Mounted Displays inIndustrial Context

    Abstract

    The application of augmented reality in industrial environments requires an effective

    visualization of text on a see-through head-mounted display (HMD). The main contri-

    bution of this work is an empirical study of text styles as viewed through a monocular

    optical see-through display on three real workshop backgrounds, examining four colors

    and four different text styles. We ran 2,520 test trials with 14 participants using a

    mixed design and evaluated completion time and error rates. We found that both pre-

    sentation mode and background influence the readability of text, but there is no inter-

    action effect between these two variables. Another interesting aspect is that the pre-

    sentation mode differentially influences completion time and error rate. The present

    study allows us to draw some guidelines for an effective use of AR text visualization in

    industrial environments. We suggest maximum contrast when reading time is impor-

    tant, and the use of colors to reduce errors. We also recommend a colored billboard

    with transparent text where colors have a specific meaning.

    1 Introduction

    A valuable application of augmented reality (AR) in an industrial contextis to superimpose technical information on the real world. The main advantage

    of this approach when compared to paper/screen-based documentation is that

    the added graphics is co-located and visualized in real time. This feature is very

    useful to support complex maintenance or assembly processes where most of

    personnel time is spent retrieving technical task instructions, localizing parts,

    and operating on them in the right order (Uva, Cristiano, Fiorentino, &

    Monno, 2010). In this case, a solution could be offered by head-mounted dis-

    plays (HMDs) that allow task instructions to be superimposed on the real-world

    view of the operator. Two main technologies are available for HMD: (1) video

    and (2) optical see-through; these technologies have different trade-offs as

    described by van Krevelen and Poelman (2010). An optical see-through HMDcould be the ideal candidate for industrial use, because of the real environment

    awareness and ergonomics. However, optical see-through systems require a

    comprehensive study of the visual perception: color and brightness of the real

    environment visually conflicts with the color and/or contrast of the superim-Presence,Vol. 22, No. 2, Spring 2013, 171190

    doi:10.1162/PRES_a_00146

    2013 by the Massachusetts Institute of Technology *Correspondence to [email protected].

    Fiorentino et al. 171

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    posed graphical elements (see Figure 1). The main prob-

    lem of the current technology is that only bright objects

    can overlap on the background. In practice, dark colors

    appear as semi-transparent and they mix in with the

    background. This makes the use of see-through HMD

    very challenging, especially in outdoor environments,

    where the brightness of the background overcomes the

    brightness of the display.

    Industrial environments usually are indoor and charac-

    terized by controlled lighting, as given by the standard

    ISO 8995-1 (ISO, 2002), but visibility problems com-

    monly arise in the readability of the technical text labels

    and these limit the effectiveness of this technology.Literature on the readability of simple text in optical

    see-through HMDs is scattered among different disci-

    plines (computer graphics, humancomputer interac-

    tion, etc.) and usually it addresses general problems

    without satisfying the specific requirements and con-

    straints of industrial workspaces (e.g., standard color

    coding, industrial practice, and workshop backgrounds).

    It is common practice in industry to follow standard or

    personalized color coding rules. For example, the 5S,

    one of the most popular workplace organization meth-

    ods, suggests the use of colors in workspace to enforcesorting, straightening, systematic cleaning, standardiz-

    ing, and sustaining (Hirano, 1995). A very common

    practice of industrial data visualization which can be sup-

    ported by AR technology is the use of shop floor paper

    tags (see Figure 2). The tags carry important production

    information in a cheap, simple, and effective way by text

    and color coding. In an aerospace facility, red tags mean

    defective products, green tags identify items to be

    repaired, and yellow tags classify products that passed

    quality control tests and are ready to be shipped out.

    Another relevant example of color coding in industrial

    practice is in piping. The standard colors for industrial

    piping are given in ASME A13.1 (ASME, 2007) which

    describes the content of the pipes, potential hazards, and

    the direction of flow. Properly labeled pipes improve

    safety and productivity by providing employees and

    emergency responders with key information. An AR-

    based visualization system can be very supportive by fil-

    tering the technical database and displaying or labeling

    only the pipes of interest to the user. A further industrial

    reference is the standard ISO 3864 (ISO, 2011a), whichdefines safety colors and safety signs for graphical sym-

    bols. It describes design principles for safety signs and

    markings, product safely labels, graphical symbols, and

    their colorimetric and photometric properties. Another

    well-known color coding scheme in industry is the

    OSHA safety color code for marking physical hazards

    (29 CFR 1910.144; OSHA, 2007). This standard states

    that red identifies fire protection equipment, emergency

    stop devices, and containers holding dangerous materi-

    als. Yellow indicates physical safety hazards, such as strik-

    ing against, stumbling, falling, tripping, and so on.All these standards refer specifically to colors of

    printed/painted supports, and not as visualized on digi-

    tal displays. In order to fulfill color coding when passing

    to an AR-based information system, a methodical

    approach is needed. In fact, specific guidelines are

    needed for AR devices where color perception could

    Figure 1. Text and crosshair superimposed on the HMD during the

    user tests.

    Figure 2. Example of industrial color tag commonly used in manufac-

    turing.

    172 PRESENCE: VOLUME 22, NUMBER 2

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    change, as opposed to printed signs; and as color percep-

    tion varies, so would readability. Industrial applications

    would benefit from these guidelines.

    Previous work reports general optimization of visual-

    ization, without providing color-based readability

    guidelines. The main goal of the presented work is to

    study the readability of textual information in indoor

    industrial environments with an optical see-through

    HMD. Different colors and text styles were combined

    to investigate textual visualization on industrial back-

    grounds. For this purpose, we developed an open-

    source test workspace to support readability test experi-

    ments and made it available to the academic community

    (Fiorentino, 2012).

    The paper is organized as follows. In Section 2, we

    present previous literature, followed by the description

    of our approach in Section 3. In Sections 4 and 5, we

    present the design of experiments, the results, and a

    related discussion. Finally, we present a conclusion and

    future work in Section 6.

    2 Related Work

    The readability of text is strictly related to aspects

    of human cognition and perception. In particular,

    human beings are sensitive to the contrast between text

    and the background on which text is superimposed(Legge, Parish, Leubker, & Wurm, 1990). In fact, the

    International Standards Organization ISO 9241 stand-

    ard-3 (ISO, 1993) recommends a minimum luminance

    ratio of 3:1 and a preferred value of 10:1. Text readabil-

    ity is a complex problem and involves different sciences

    (e.g., cognitive research, psycholinguistics, and human

    factors). Physiological and psychological effects influ-

    ence text readability on displays, as demonstrated by

    Fukuzimi, Yamazaki, Kamijo, and Hayashi (1998). They

    studied the physical parameters that influence human

    color perception on CRT displays: dominant wave-length, stimulus purity, and luminance. They analyzed

    results from subjective evaluations combining: (1) some

    dominant wavelengths, (2) stimulus purities, and (3)

    luminance. They also studied the readability of colors

    using an objective method using measurements from

    electroencephalogram signals. Their results demon-

    strated that an optimal stimulus purity exists in each

    dominant wavelength, and further, that it is independ-

    ent of luminance.

    Harrison and Vicente (1996) explored text readability

    in the design of transparent 2D menus superimposed

    over different graphical user interface (GUI) background

    content. They presented a novel anti-interference (AI)

    font that uses luminance values to create a contrasting

    outline. Their work includes an empirical evaluation of

    the effect of varying transparency levels, visual interfer-

    ence produced by different types of background content,

    and the performance of AI fonts on text-menu selection

    tasks. Testing demonstrated that the closer that the

    shade and hue of the background is to the text color, the

    higher is the interference, and the detriment of the

    resulting performance. AI fonts produced a substantially

    flatter performance curve, shifted toward better (i.e.,

    faster) performance, especially at higher transparency lev-

    els (i.e., over 50%), which is exactly the condition we

    have in a see-through display.

    A basic study on the perception of gray text on a non-

    uniform gray background was presented by Petkov and

    Westenberg (2003). They conducted psychophysical

    experiments to demonstrate that the spatial frequency of

    the patterns in the background has a relevant effect on

    readability. The masking effect of the background is

    higher when its characteristic pattern width is compara-ble to the letter stroke (or weight), while the letter size

    shows no main effect. Their research can be a valid justi-

    fication for the use of the outline style, and even more of

    a billboard, which removes the background texturized

    pattern around the text strokes. Nevertheless, this

    research does not address color issues.

    A more specific study on text readability for AR appli-

    cations was presented by Leykin and Tuceryan (2004)

    using a calibrated desktop CRT monitor at an approxi-

    mate distance of 50 cm from the users head. They

    implemented seven real-time supervised classifiers,trained them with user data, and evaluated whether text

    placed on a particular background was readable or not

    on the screen. They concluded that textured background

    variations affect readability only when the text contrast is

    low. Their study considered only the luminance informa-

    tion of grayscale images and not different color.

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    An interesting study of augmented reality viewability

    in outdoor environments was conducted by Gabbard,

    Swan, and Hix (2006). They evaluated text legibility

    using an optical see-through display and different text

    styles superimposed on matte-finished printed poster

    backgrounds (40 in 60 in). They used six text drawing

    styles, three static and three active (meaning that the text

    color changed depending upon the presented back-

    ground poster), six backgrounds (pavement, granite, red

    brick, sidewalk, foliage, and sky), and three distances

    (1 m, 2 m, and 4 m from the user). Their approach pre-

    sented three different active algorithms to determine the

    best color to use: Complement, Maximum HSV Com-

    plement, and Maximum Brightness Contrast. They

    chose blue text to replace black text, which is impossible

    to produce on see-through displays. Their most impor-

    tant finding was the empirical evidence that user per-

    formance is significantly affected by background texture,

    text drawing style, and their interaction. The billboard

    drawing style (blue text on pure white), and green text,

    provided the fastest performance. Visually complex back-

    ground textures performed very well (red brick) and

    intermediately well (foliage), contradicting the initial

    hypothesis that a complex background must reduce per-

    formance. Surprisingly, the active text drawing styles did

    not perform better than the static styles in the practical

    tests. Their final guidelines suggested the use of fullysaturated green labels, and the avoidance of fully satu-

    rated red labels. An important aspect for our research is

    that the error rate was very small (1.9%), and they did

    not analyze it further.

    Tanaka, Kishino, Miyamae, Terada, and Nishio

    (2008) proposed an unusual approach to address optical

    see-through HMD limitations by using a fixed camera

    mounted on the visor and directed forward. The camera

    faced two mirrors that separated the left and right view.

    Their approach was based on using the camera to evalu-

    ate the peripheral visibility of the user periphery and asuggestion to turn the head whenever this would lead to

    better conditions. Their visibility model considered: (1)

    the average of RGB and HSV color spaces, (2) the var-

    iances in RGB, YCbCr, and HSV color spaces, (3) how

    information was tied to a precise area, and (4) which

    movements were possible. However, their layout strat-

    egy expressly did not preserve the registration of the dig-

    ital information on the real objects.

    Jankowski, Samp, Irzynska, Jozwowicz, and Decker

    (2010) explored the effects of varying four text drawing

    styles (plain, billboard, anti-interference, and shadow),

    image polarity (positive when dark characters are on a

    light-colored panel and conversely for negative), and

    two backgrounds: the first one with videos recorded in

    urban and outdoor environments and the second one

    recorded in 3D video games. They found out that there

    was little difference in reading performance for video and

    3D backgrounds. Furthermore, they concluded that

    negative presentation is faster and more accurate than

    positive presentation. Therefore, billboard styles resulted

    in the easiest to read and the most immune to back-

    ground distractions.

    From the presented works, we can conclude that the

    knowledge on the readability of text on HMD is scat-

    tered among different disciplines, application fields, and

    hardware setups, and, at the moment, it is not adequate

    to provide standard and reliable guidelines for the appli-

    cation developers. In particular, we found no previous

    work addressing the specific industrial environments.

    This study draws inspiration from Gabbards experi-

    ments that address mainly outdoor environments and

    textures. Our idea is to apply a similar approach to indus-

    trial context. Therefore, the motivation of this work is tostudy and find effective text styles for monocular optical

    see-through presentation, specifically for the industrial

    context.

    3 Our Approach

    We used a mixed-design approach to examine user

    performance in a text-identification task similar to Gab-

    bards test (Gabbard, Swan, Hix, Kim, & Fitch, 2007).

    Gabbard considered text as one of the most fundamental

    graphical elements in any user interface and therefore theidentification task is text-based (as opposed to icon-,

    line-, or bitmap-based).

    Because of the limited previous work on displaying in-

    formation in AR in an industrial context, we wanted to

    focus on text readability in real workshop scenarios. Spe-

    cifically, we designed an experiment that abstracted the

    174 PRESENCE: VOLUME 22, NUMBER 2

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    short reading tasks that are very common in technical

    AR applications in industry. For this study, we used a

    low-level identification and visual search task, since we

    did not want to address the semantics (e.g., cognitively

    understanding the contents/meaning of the text). We

    simply evaluated whether or not users could quickly and

    accurately read information (i.e., text legibility), asking

    the user to perform the following tasks.

    Scan a meaningless short random text string.

    Identify a target letter.

    Count letters.

    Provide a response.

    The user is asked to perform these tasks in different

    presentation modes, obtained with text styles and colors

    used to convey mandatory information for the aforemen-

    tioned industrial motivations. In this work, we limited

    text styles to four types: (1) simple text, (2) text with

    outline, (3) text with billboard, and (4) text with outline

    and billboard. The text, outline, and billboard can all be

    of different colors. The combination of text styles and

    colors creates the presentation modes used for our

    experiment, which will be detailed in Sections 4 and 5.

    We also want to make it clear that the experiment task is

    a foreground-only activity, and we did not measure any-

    thing about the users awareness of background content

    or changes (i.e., this was not a divided attention task).In the initial stage, we ran preliminary tests needed to

    detect the most significant parameters to be used as the

    independent variables of our experiments. We used an

    optical see-through HMD, the Liteye LE 750A, 800

    600 OLED display. However, the parameters involved

    in the visualization of the text (text font, color, size,

    position, etc.) are too numerous for extensive user tests.

    For this reason, we developed a specific software tool,

    called HMD test, written in C using the Qt library,

    which has two main functions: editing the parameters

    (editor mode) and running the user tests (player mode).In the editing phase, the user can interactively change

    all the parameters of the text visualization with a simple

    GUI and preview the final effect in real time on the mon-

    itor (see Figure 3).

    If the HMD is connected to the second video port,

    the user can preview the visualization directly; otherwise

    he or she can simulate the result by loading a back-

    ground image on a desktop screen.

    The user can change and test the following text style

    parameters: font size, text color and transparency, text

    billboard color and transparency, outline width, and out-

    line color and transparency. In our preliminary phase, we

    simulated different configurations using a library of 100

    pictures downloaded from the internet using Google

    Images with specific keywords (i.e., workshop, shop

    floor, manufacturing, etc.; see Figure 4). In this way, we

    were able to evaluate the experimental settings and to

    plan the user tests.

    The HMD testbed automated the execution of tests in

    player mode: the test configurations are retrieved from

    the test template file, shuffled randomly, and then dis-

    played on the HMD. The application acquires and ar-

    chives the following data in a simple log file: subject

    username, time and date of the test, displayed text

    strings, text style, users answer, and response time.

    During the test, the performance, the progress bar,

    the current score, and the top score are displayed on the

    service desktop screen to monitor the test (see Figure

    5). The score is added to motivate the user to maximize

    performances during the test; since it is not visible to

    the participants, it cannot influence the test results. The

    software is publicly available on our website

    (Fiorentino, 2012). We are interested in comparing theresults from other researchers using different display

    configurations.

    4 Design of Experiment

    We differentiate our experiments from Gabbards

    tests by the usage of real industrial backgrounds. Our

    software displays two different text blocks on the HMD

    view area. The upper text block is composed of three

    randomly generated strings with alternating uppercase

    and lowercase letters, while the lower block consists ofthree strings of capital letters. In the upper block, one of

    the three sets of letter pairs consists of the same letter,

    given once as uppercase and once as lowercase (e.g.,

    mM, Pp). This is the target letter. Each user has to iden-

    tify the target letter and he or she has to count out how

    many times it appears in the lower block. The partici-

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    pants should input the result on a provided numeric key-

    pad. The possible answers are 1, 2, 3, or 0 in the case of

    unreadable letter not found. The alphabet is restricted to

    the following letters: C, K, M, O, P, S, U, V, W, X, Z.

    These letters have graphical similarity in uppercase and

    lowercase, therefore this restriction makes the difficulty

    associated with the target identification uniform. Our

    software generates and visualizes the text blocks on theHMD and records response time and user errors. A

    crosshair viewfinder is displayed, and the user must point

    to a specific target in the real scene (see Figure 1). This

    solution avoids the chance that users may turn the HMD

    to a more favorable position (i.e., to choose a specific

    background point).

    4.1 Measures

    We focused on the following experiment inde-

    pendent variables (see Table 1) and the dependent varia-

    bles (see Table 2) that we collected for the subsequent

    statistical analysis.

    Apart from measuring efficiency (completion time)

    and effectiveness (error rate), a 5-point Likert scale is

    used to measure user preferences with a post-experiment

    questionnaire.

    4.2 Backgrounds

    Most of the industrial backgrounds we have

    encountered, especially those related to production

    Figure 3. HMD testbed in editor mode: user can design the text style and preview it on the screen or HMD.

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    facilities, present some common characteristics. They areindoors, uniformly lit, quite dirty, and mainly gray in

    color. They often present sparse saturated colors (e.g.,

    tools, signs, etc.) We used three real-world backgrounds:

    (1) testbed frame, (2) tool workbench, and (3) motor-

    bike engine (see Figure 6).

    We chose the backgrounds with the intention to pro-

    vide three different luminance profiles: negative

    (testbed), positive (tool workbench), and neutral

    (engine). We took pictures from the users point of view

    with a digital camera and we display in Figure 7 the

    related histograms. The test was carried out with the usersitting on a swivel chair in order to have the same head

    position for all participants of about 50 cm in height and

    60 cm in depth from the background center point (see

    Figure 8). All tests were performed in a laboratory with

    fully shaded windows and artificial lighting (overhead

    fluorescent lights). We measured the illuminance value

    with a lux meter and we registered an average illumina-tion over the work area of about 300 lux.

    4.3 Colors

    Our setup required users to be concentrated for

    the whole task. Studies on the length of human sustained

    attention reported a maximum of around 20 min for

    adults (Cornish & Dukette, 2009). To keep the experi-

    ment time within 20 min, we limited the color range to

    only four options. In particular, following the specifica-

    tions defined by the ISO 3864 standard (colors for safetysigns; ISO, 2011a), we decided to use the red and the

    green as safety colors (i.e., colors with special properties

    to which a safety meaning is attributed), and white and

    black as contrast colors. Apart from their general mes-

    sages (safety and prohibition), green and red are worth a

    deeper investigation in AR setup because the literature

    Figure 4. A sample of the images used in the preliminary design phase.

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    reports green as one of the best colors for reading, and

    red among the worst performing colors on CRT displays

    (Fukuzimi et al., 1998). Colors are displayed on our

    uncalibrated optical see-through HMD. We tested the

    following colors, defined in an RGB color space.

    White: RGB (255,255,255). Red: RGB (255,0,0). Green: RGB (0,255,0). Black: RGB (0,0,0).

    An important issue is the visualization of the color

    black on the optical see-through HMDs. In fact, theRGB (0,0,0) means, in additive color composition, that

    all the display pixels are off, so it will be transparent on

    an optical see-through device. Therefore, in this work,

    when we speak about the color black, we mean no

    added color, which is the background color bleeding

    through these transparent pixels. In our presentation

    Figure 5. Player mode: during the test, the service screen (not seen by the participant) shows the list of configurations

    (center), progress bar (lower left), and the user performance and top score.

    Table 1. Independent Variables of Our Experiment

    Independent variables

    Participant users 14 11 males, 3 females

    Backgrounds 3 Testbed frame, tool workbench, engine

    Text styles 4 Text only, with outline, with billboard, with outline and billboard

    Colors 4 Black, green, red, white (when applicable)

    Repetitions 5 Five for each presentation mode and background

    Total trials 2,520 14 3 12 5

    Table 2. Dependent Variable of Our Designed Experiment

    Dependent variables

    Completion time in ms

    Error rate Correct task completion 1,

    wrong task completion 0

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    modes, the color black can only be used as text or as out-

    line on a differently colored billboard, because a black

    billboard is equivalent to no billboard. Our experimental

    indoor environment has a low illuminance (around 300

    lux); therefore, the transparent stroke of black text or ofthe outline is perceived as dark enough to be called

    black. With this meaning we considered black as a color

    in our experiment. Our purpose is to study its perform-

    ance as contrast color to background colors (i.e., white,

    red, and green), as indicated by ISO standards (ISO

    2011a, 2011b, 2004).

    4.4 Text Styles

    Font type and size were not considered as variables.

    We chose the sans serif Helvetica font because it was

    used in most of the readability experiments in the litera-ture and we chose 22 points as the font height as the

    smallest size that we can clearly read in the pre-test

    phase. Indeed, we focused on four different text styles.

    There are two well-known techniques in the literature to

    isolate text from a variable background: the outline,

    inspired by Harrisons AI font, and the billboard, which

    Figure 6. The three real backgrounds used in the tests: testbed frame (left), tool workbench (center), motorbike engine (right).

    Figure 7. Normalized luminance histograms (number of pixels for each luminance value) of the pictures of the background used in the test:

    negative (testbed frame), positive (tool workbench), and neutral (engine).

    Figure 8. The experiment setups for the three backgrounds.

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    has proven effective but costly in terms of pixels. We

    used four main text styles in our experiments: the first,

    the simplest, is text only; the second is text with a 2-

    point-wide outline; the third is text with a rectangular

    billboard; and finally, the fourth is text with a combina-

    tion of outline and billboard.

    Table 3 shows the 12 presentation modes used in the

    experiments preliminarily selected from the possible ones

    changing text color (black, green, red, white), outlinemodes (black, i.e., transparent, green, red), and billboard

    mode (green, red, white).

    4.5 Participants

    Fourteen unpaid participants were recruited for the

    study among undergraduates in technical subjects. They

    were 11 males and three females with the following age

    distribution: seven from 21 to 25 and seven from 26 to

    30, with an average age of 25. Six participants wore

    glasses but none had color deficiency. All were right-eyedominant. The users wore the HMD in front of the right

    eye, and they received adequate instruction and per-

    formed a trial session. The participants could discontinue

    the test at any time and the break time was not limited.

    The subjects performed a total of 2,520 trials (14 partici-

    pants 12 permutation modes 3 backgrounds 5

    repetitions) ensuring a Latin square design. Each subject

    saw, on each background, a total of 60 visualization

    queries. At the end of the complete trial, each participant

    filled out a questionnaire to detect particular problemsand to collect evaluations and opinions.

    4.6 Apparatus

    Our hardware system for experimental tests con-

    sisted of the following.

    Notebook HP Pavilion dv6-6150sl Entertainment

    Notebook PC, Intel Core i5-2410M, 2.30 GHz,

    RAM: 4 GB di DDR3, graphics card: AMD Radeon

    HD 6770M with Windows 7 and HMD test soft-

    ware. Viewer Liteye LE 750A, OLED display, 800 600

    60 Hz, contrast 100:1, transmission 70/30,

    luminance 300 cd/m2, 288diagonal FOV, mounted

    on ergonomic support, and connected by VGA (see

    Figure 9). We set the diopter adjustment to 0 for all

    users. Wireless numeric keypad by Targus, model

    AKP02EU, battery powered, to collect participants

    answers.

    4.7 Hypotheses

    Prior to conducting the study, we formulated the

    following hypotheses.

    H1. Different workshop backgrounds will affect user

    test performance (completion time and error): text

    readability is background-dependent.

    Table 3. The 12 Presentation Modes Used in the Experiments

    Text

    color

    Outline

    color

    Billboard

    color

    1 Black Green

    2 Black Red

    3 Black White

    4 Green

    5 Green White

    6 Green Black White

    7 Red White

    8 Red

    9 Red Black White

    10 White

    11 White Green

    12 White Red

    Figure 9. The optical see-through HMD used in our experiments

    (Liteye LE 750A).

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    H2. The presentation mode will affect performance.H3. Text style will affect performance.

    H4. Text color will affect performance.

    H5. Outline color will affect performance.

    H6. Billboard color will affect performance.

    5 Results

    We analyzed the acquired data to evaluate the main

    effect of background, color, and text style on readability

    performance. We used quantitative and qualitative data.

    The completion time and error rate were quantitativedata, while the subjective responses were the qualitative

    data. In a preliminary phase of the analysis, we removed

    the outliers with the Tukeys outlier filter based on the

    interquartile range. To make statistical inferences, we

    started to inquire whether the completion time data fol-

    lowed a normal distribution. We used the ShapiroWilk

    normal test, the AS R94 algorithm, which rejected the

    normal distribution for all the samples (p< .05). The

    skewness analysis showed a positive value for all samples:

    this is typical in task-time-completion measures that fol-

    low a lognormal distribution. We log10-transformed allthe completion times prior to statistical analysis. To eval-

    uate the homoscedasticity, we applied the Levene test,

    because this test does not require equal dimensions for

    all the groups.

    As to the error rate, the faults considered in our analy-

    sis are users wrong answers. We used the method of

    N 2 contingency tables to do statistical inference (p

    .05) on error data. We used the following error rate defi-

    nition.

    ER% Number of errorsNumber of targets

    100

    Each sample of 12 modes could have 70 possible

    errors (14 participants 5 repetitions), that is, the num-

    ber of targets in the error rate definition.

    In the following sections, we detail the results as to

    the background effect, the text style effect, and the color

    effect with a discussion on how to optimize readability

    when the color message is required, such as for safety

    warning.

    5.1 Background Effect

    With regard to completion times, the ANOVA

    showed a main effect of background,F(2 2442)

    49.377;p< .001. Figure 10 shows the box plot of the

    completion times. On each box, the central mark is the

    median, the edges of the box are the 25th and 75th per-

    centiles, the whiskers extend to the most extreme data

    points not considered outliers, and the outliers are plot-

    ted individually. Considering the mean completion time,

    the engine background had times 13% lower than the

    tool workbench and 17% lower than the testbed frame.The application of the ShapiroWilk test revealed that

    the answer distributions for the three backgrounds were

    not all normal, and homoscedasticity was not verified.

    Therefore, we applied the Friedman test because it is

    more indicated than ANOVA in these conditions. The

    Friedman test showed a significant difference among the

    three backgrounds (see Table 4). We used as the post

    hoc pair-comparison the Wilcoxon signed-ranked test,

    with Bonferroni correction (a 0.017), which con-

    firmed that the engine background had the lowest

    response time (with respect to the testbed frame,Z

    22.601,p< .001; with respect to the tool workbench,

    Z 23.907,p< .001), while the testbed frame back-

    ground had statistically the highest answer time (with

    respect to the tool workbench,Z 23.526,p< .001).

    An explanation could be that neutral backgrounds are

    the best for text readability. As a result, we can confirm

    Figure 10. Box plot of the completion times for the three backgrounds

    (the X marks the mean of samples).

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    the hypothesis H1 relative to the completion time. Textreadability depends on the background.

    As to the error rates for the three different back-

    grounds, we computed an average error of 6.67% on the

    testbed frame background, 7.26% for the tool work-

    bench background, and 6.55% on the engine back-

    ground (see Figure 11).

    Comparing the three sample error rates with contin-

    gency tables, we did not find statistically significant dif-

    ferences among the three backgrounds, w2(2) 0.3869

    5.991. Unlike the completion-time analysis, this

    result, limited to error rates, does not support hypothesis

    H1.

    5.2 Presentation Mode Effect

    With regard to completion, the results in Table 5

    about normality and homoscedasticity begged the appli-

    cation of the Welch ANOVA test for all of the 12 combi-

    nations, and the GamesHowell test for the 66 pair com-

    parisons. Differences among all samples were statically

    shown, thus hypothesis H2 was supported (see Figure

    12).

    The fastest presentation modes resulted from the black

    text, no outline, and white billboard (mode 3). The best

    performance of mode 3 was statistically confirmed

    against modes 9, 7, 4, 11, and 8 (d 0.092,p .001).

    It is important to note that mode 5 (green, no, white)

    and mode 6 (green, black, white) showed no statistical

    difference,F(1) 0.012,p 0.912, while mode 7 (red,

    no, white) and mode 9 (red, black, white) are not

    strongly different,F(1) 3.962;p .047. This confirms

    that the black color is effectively not a color since it

    shows no main effect if used as an outline.

    An error-rate comparison (see Figure 13) among allthe 12 combinations revealed a statistically significant

    difference,w2 (11) 37.811 > 19.675, allowing us to

    accept hypothesis H2; but in this case, the performance

    distribution is different from the completion-time analy-

    sis. The best performing modes were modes 2, 5, and

    12. An interesting result is that presentation modes dis-

    playing red text (modes 7, 8, and 9) had bad scoring,

    both for completion times and error rates.

    5.2.1 Interaction BackgroundPresentation

    Mode. We tested the interaction between the back-ground and the presentation-mode effects with a two-

    ways unbalanced ANOVA, which showed that there is

    no interaction effect,F(22,2427) 0.778;p 0.756.

    Every presentation mode displays results respecting the

    background ranking (with the partial exception of modes

    2 and 8) as shown in the radar plot in Figure 14.

    Table 4. Background Data Analysis

    Background Testbed frame Tool workbench Engine

    ShapiroWilk test W

    0.996 W

    0.997 W

    0.995p .031 p .097 p .015

    Levene test F(2,2442) 9.895;p< .001

    Mean rank 2.95 2.01 1.03

    Friedman test w2(2) 1518.9;p< .001

    Figure 11. Box plot of error rate for the three backgrounds (the X

    marks the mean of samples).

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    Table 5. Completion-Time Analysis of the Presentation Modes, Sorted by the Mean Response Time

    Presentation

    mode

    Mean response

    time (ms) ShapiroWilk test

    Levene

    test

    Welch

    ANOVA test

    3 (black,-,white) 5,441 W(205) 0.996 p .841

    12 (white,red,-) 5,909 W(209) 0.989 p .119

    1 (black,-,green) 6,108 W(207) 0.992 p .306

    6 (green,-,white) 6,109 W(203) 0.992 p .385

    10 (white,-,-) 6,112 W(197) 0.994 p .599

    5 (green,-,white) 6,139 W(208) 0.995 p .764 F(11,2451) 4.359 F(11) 12.653

    2 (black,-,red) 6,239 W(206) 0.990 p .189 p< .001 p< .001

    8 (red,-,-) 6,728 W(206) 0.990 p .189

    11 (white,green,-) 6,864 W(204) 0.990 p .184

    4 (green,-,-) 6,966 W(209) 0.995 p .763

    7 (red,-,white) 7,276 W(205) 0.997 p .927

    9 (red,black,white) 7,914 W(204) 0.996 p .813

    Figure 12. Box plot of completion time for each presentation mode (the X marks the mean of samples).

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    Figure 13. Box plot of error rate for each presentation mode (the X marks the mean of samples).

    Figure 14. Radar plot of response times (ms) for the three backgrounds in the 12 presentation modes.

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    5.3 Text Style Effect

    To analyze the effect of text styles, we gathered all

    the presentation modes in four groups, as presented in

    Table 6. The completion-time analysis showed that the

    data did not pass the homoscedasticity test; therefore,

    we used the Welch-ANOVA test, which revealed a signif-

    icant difference among the styles (see Table 6). This

    result allowed us to accept hypothesis H3.

    The GamesHowell post hoc test showed clearly that

    the text and billboard style performed better than the

    text-only style and the text outline and billboard style.

    The text and outline style is better than only the worst

    style: text outline and billboard (see Table 6). As to error

    rates, there is no significant difference among the text

    styles,w2(3) 5.14 < 7.82.

    5.4 Color Effect

    5.4.1 Text Color. We explored the text colors by

    collecting all data in four groups: black (1, 2, and 3),

    green (4, 5, 6), red (7, 8, 9), and white (10, 11, 12).

    The results are represented in Figure 15. The compari-son of these four samples gave as the result that the color

    black seems to outperform all other colors; ANOVA:

    F(3,2448) 25.420,p< .001, but indeed, good per-

    formance can be attributed to the presence of the bill-

    board, which is always associated with black text, as

    reported in Section 5.3. Therefore, we removed the

    black group and proceeded to the comparison of green,

    red, and white colors for text.

    The ShapiroWilk test showed that all groups had a

    normal distribution, but the Levene test showed that the

    variances were different (see Table 7).

    In this case, we applied the Welch-ANOVA test to

    compare the three samples. There was a statistically sig-

    nificant difference in the text color choice; thus, hypoth-

    esis H4 is accepted. The white text group had (see Fig-

    ure 16) the lowest answer time. GamesHowell post hoc

    tests allowed pair-wise comparisons, and they revealed

    that: (1) the green text color group is statistically betterthan red (d 0.052,p< .001); (2) the white text color

    is better than red (d 0.061,p< .001).

    As to error rate, there were statistically significant dif-

    ferences among the three color text samples: w2(2)

    7.563 > 5.991. The green text group has the minimum

    average error rate, at 6.03%, compared to 6.34% for the

    white text group, and 9.68% for the red text group (see

    Figure 16). The black (transparent) text group has an av-

    erage error rate of 5.24%. This is in accord with hypothe-

    sis H4.

    Moreover, the red text group did not perform as wellas the other colors, as confirmed by completion-time

    results and as reported in the previous literature.

    5.4.2 Outline Color. As stated in Section 5.2,

    the color black, for the reasons discussed in that section,

    shows no main effect if used as the outline. Therefore,

    Table 6. Text Style Comparison

    Text style

    Text only

    (T)

    Text and

    outline (TO)

    Text and

    billboard (TB)

    Text outline and

    billboard (TOB)

    Presentation modes 4, 8, 10 11, 12 1, 2, 3, 5, 7 6, 9

    ShapiroWilk test W 0.996 W 0.994 W 0.997 W 0.996

    p .101 p .092 p .072 p .406

    Levene test F(3,2249) 5.944;p< .001

    Mean (ms) 6,622 6,368 6,237 6,887

    Welch-ANOVA test F(3) 5.977;p .001

    GamesHowell test TB better than TOBd 0.042,p .001

    TB better than Td 0.026,p .022

    TO better than TOBd 0.034,p .035

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    we could compare only the green and the red outline

    (mode 11 and mode 12) applied on white text. The sta-

    tistical results support hypothesis H5 (12 better than

    11) for both completion-time (GamesHowell post hoc

    test:d 0.065,p .002) and error-rate (see Figure 16)

    analyses. The red outline performs better than the green

    outline, probably because of a higher contrast between

    the text and the outline.

    5.4.3 Billboard Color. Next, we wanted to ana-

    lyze what was the best billboard among the three combi-

    nations available under the black text group. Therefore,

    we kept the black text and focused our attention on con-

    veying the color information using the billboards (green,

    Table 7. Text Color Analysis

    Text color Green Red White

    ShapiroWilk test W(617) 0.998 W(611) 0.997 W(606) 0.996

    p .653 p .248 p .103

    Levene test F(2,1831) 3.688p .025Means (ms) 6,412 7,228 6,281

    Welch-ANOVA test F(2) 21.344p< .001

    Figure 15. Box plot about scattered completion-time data referring to each text color group (the X marks

    the mean of samples).

    Figure 16. Box plot of error rate for each grouped text color presenta-

    tion mode (the X marks the mean of samples).

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    red, and white). For these presentation modes we had

    three normal distributions and homogeneity for variance

    (see Table 8).

    The one-way ANOVA revealed a statistically signifi-

    cant difference among modes 1, 2, and 3, that is, black

    text, no outline, and green, red, and white billboards,

    thus accepting hypothesis H6 on billboard color influ-

    ence (see Figure 17). Tukey post hoc tests confirmed

    statistically significant differences between mode 3 and

    mode 1 (d 0.050,p .009), and between mode 3 and

    mode 2 (d

    0.059,p

    .017) showing the best presen-tation mode with back text over a white billboard.

    5.5 Qualitative Results

    The post-experiment questionnaire was composed

    of two parts, both using a Likert scale. The subjects were

    presented with the stimuli as reminder. In the first part,

    the participant had to mark every presentation mode

    with a vote from 1 to 5. In the second part, the partici-

    pant answered questions about his or her opinions using

    five judgment values: not at all, a little, on average,

    enough, much. Figure 18 shows the cumulative

    responses of the user interviews.

    5.6 Discussion

    A first result is that the real industrial background

    (300 lux) influenced text readability with regard to

    completion time. This is in accordance with previous

    work that used different setups: a printed poster and

    video on display monitors. Unlike the completion time,

    the analysis of the error rates showed that backgrounddid not have a significant influence. This last aspect

    should be further investigated, because our results are in

    contrast with general expectation and previous results

    (e.g., Jakowski et al., 2010). Gabbards tests (Gabbard

    et al., 2006), which are closer to our setup, revealed an

    error rate that was not significant, and therefore they

    ignored it in the statistics. Our tests, on the contrary,

    showed higher error rates (6.54% vs. 1.9%). The engine

    background performed better than the other two. This

    result may depend on several factors, including lumi-

    nance profile, which, in the specific case, is neutral,unlike the other two. The presentation mode showed a

    main effect on both completion time and error rate. This

    result is in accordance with the literature. A non-trivial

    statistical outcome is that there is no interaction effect

    between background and presentation mode. This result

    is in contrast to previous findings in outdoor environ-

    Table 8. Comparison of Billboard Colors (Black Text)

    Black text on billboard

    Green Red White

    ShapiroWilk test W(207) 0.992 W(206) 0.990 W(205) 0.996

    p .306 p .189 p .841

    Levene test F(2, 615) 0.126p .882

    Means (ms) 6,109 6,237 5,445

    One-way ANOVA F(2, 615) 7.061p .001

    Figure 17. Box plot of scattered completion-time data for black text

    color and the three different billboard colors (the X marks the mean of

    samples).

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    ments. Gabbard, Zedlitz, Swan, and Winchester (2010)

    found strong interactions between background and dis-

    play color. However, these outcomes were reported for

    outdoor environments with 8001000 lux. In our opin-

    ion, our result justifies the efforts in finding an optimal

    presentation mode, since it will be independent from the

    background when the luminance of the display is much

    brighter than the environment, as in an indoor industrial

    background.

    Among all the presentation modes, the text stylerevealed a main effect on completion time but not on

    error rates. Post hoc analysis showed that the billboard is

    more effective than outline or text only, in accordance

    with the literature. The good performance of the bill-

    board has a drawback in terms of scene occlusion.

    The results achieved revealed that completion time,

    error rate, and user interviews are not coherent in defin-

    ing a unique ranking of presentation modes. According

    to response time, the best results are obtained by mode

    3 (black text, white billboard), and by mode 12 (white

    text, red outline). The third-best performer is mode 1(black text, green billboard), very similar to mode 3. An

    explanation can be found in the higher contrast between

    text and background in accordance with the ISO recom-

    mendations about text readability. We validated, in the

    industrial context, the principle of using maximum con-

    trast in order to achieve fast readability on a see-through

    HMD. Therefore, our results suggest either black text

    and white billboard or white text only when reading time

    is important, for example, for maintenance instructions.

    In contrast to the results obtained from completion

    time, the error rates showed a different ranking. The best

    results are obtained by mode 2 (black text, red bill-

    board), followed by mode 5 (green text, white bill-

    board), and by mode 12 (white text, red outline).

    Although our results suggest the use of colors when the

    information is critical and accuracy is mandatory (e.g.,warning signal), deeper study is necessary. Also, the pre-

    sentation mode qualitative ranking obtained from user

    interviews is not in concert with user quantitative per-

    formance in terms of completion time and/or error rate.

    This is quite interesting, since it proves that the user is

    not able to choose the best presentation mode in terms

    of performance. We therefore suggest in AR application

    design to prevent users from freely customizing visual-

    ization preferences. The only result that is confirmed by

    completion time, error rate, and user interview is that

    presentation modes displaying red text perform poorly,as already shown in the literature (e.g., Gabbard et al.,

    2007; Fukuzimi et al., 1998). In industrial applications,

    it can be necessary to convey specific color information

    along with the textual description. In this case, our tests

    recommend the use of a specific color for the billboard

    and black (transparent) for the text.

    Figure 18. Cumulative marks given by participants at the end of their test trials (range 15).

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    6 Conclusion

    We presented an empirical study on the readability

    of text styles using an optical see-through HMD on dif-

    ferent industrial scenarios. A preliminary test, supported

    by a software tool implemented by the authors, was used

    to explore a large number of configurations against a gal-

    lery of industrial images taken from the internet. We

    selected and tested 12 presentation modes using four

    main colors (black/transparent, white, red, and green),

    four different text styles (text only, text and outline, text

    and billboard, text and outline and billboard), and three

    different real workshop backgrounds (testbed frame, a

    tool workbench, and a motorbike engine). We ran 2,520

    test trials with 14 participants who were interviewed after

    the experiment.

    The first finding of this work is that both the presenta-

    tion mode and the background influence the readability

    of text, but there is no interaction effect between these

    two variables. An important result is that an optimal pre-

    sentation mode will work well, independent of the back-

    ground in indoor industrial lighting conditions (300

    lux). We also note that the user is not able to choose the

    best performing presentation mode, and therefore we

    recommend that an AR application should not allow the

    user to customize the visualization preferences. Another

    interesting aspect is that the presentation mode differen-tially influences completion time and error rate.

    The present study allows us to draw some guidelines

    for an effective use of AR text visualization in industrial

    environments. In particular, we suggest maximum con-

    trast styles, such as black text and white billboard or

    white text only, when reading time is important, and the

    use of colors when avoiding errors in readability is criti-

    cal. We also suggest a colored billboard with black text

    where colors have a specific meaning. Billboards provide

    the best performance, but at the cost of scene occlusion.

    Future investigation is needed to explore billboard areaoptimization. Apart from black and white colors, we

    tested only red and green. Future work will involve test-

    ing with other colors such as blue, yellow, orange, and

    so on. As a final remark, our findings are HMD-device-

    dependent, and for this reason, we provide our software

    and the test configurations presented in this paper on

    our website, in order to allow other researchers to collect

    and compare results using different devices.

    Acknowledgments

    The authors would like to thank Michele Mazzoccoli and

    Michele Gattullo for the usefulhelp provided in the test design

    and execution, and all the students who took part in the test.

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    C o p y r i g h t o f P r e s e n c e : T e l e o p e r a t o r s & V i r t u a l E n v i r o n m e n t s i s t h e p r o p e r t y o f M I T P r e s s

    a n d i t s c o n t e n t m a y n o t b e c o p i e d o r e m a i l e d t o m u l t i p l e s i t e s o r p o s t e d t o a l i s t s e r v w i t h o u t

    t h e c o p y r i g h t h o l d e r ' s e x p r e s s w r i t t e n p e r m i s s i o n . H o w e v e r , u s e r s m a y p r i n t , d o w n l o a d , o r

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