IEEE Consumer Electronics Magazine - May 2018 - 96

from 500 studies [3]. The SUS's learnability dimension also provided positive feedback. The participants felt they
did not require technical support or
need to learn a great deal to be able to
use the system.

EXPERIMENT 2: SCALABILITY
This study aimed to verify whether
BioAR could scale to a large number of
machines, particularly with respect to
perceiving visual changes. We intended
(a)
the study to verify, through empirical
evidence, that a great many devices can
be encoded and that fast-changing
states encoded with the BioAR metaphor elicit a preattentive response. We
considered a task preattentive if it performed in fewer than 250 ms. We
designed the study after Healey et al.'s
work on preattentive processing of
(c)
visual features [13]. In this respect, we
investigated target-detection tasks with
FIGURE 9. A study on scalable perception. (a) A participant observes a forest of trees, each variations in form (a palm tree or a
representing a machine. (b) A close-up of the trees. (c) The four possible aspects of a tree maple) and hue (red or green), as
for this study.
depicted in Figure 9.
The target-detection task simulated
the
search
of
a
machine
in
need of maintenance while visualmedian = 14), which was displayed using the amount of foliizing real-time data. In one task, 18 frames were displayed
age. In contrast, participants easily identified the presence or
sequentially for a fixed exposure time of 100 ms (as in [13]).
absence of the capsule (sun highlighting), the temperature
In our case, a frame contained 49 elements (7 × 7).
(foliage color), and the critical error (dead tree) (165, 168, and
We organized the study as a within-participant experiment
167 out of 168, respectively; median = 14). Seven of the subwith the feature (hue or form) as the independent variable
jects said it was hard to distinguish between the four different
and the error as the dependent variable. Healey et al. studied
states related to the machine maintenance status based on the
the performance of visual features with two-dimensional
size of the flowers. One commented that adding more dimen(2-D) shapes and found that hue variations lead to fewer
sions (increasing the number of sensors) would create a more
errors. Thus, we assumed that participants would make sigcomplex visualization, making it harder to interpret. In general,
nificantly fewer errors in the hue than in thew form condition.
though, these results indicated that the metaphor is readable.
All 12 subjects felt that interacting with BioAR was
pleasant (five strongly agreed and seven agreed). Moreover,
PROCEDURE
the participants thought that the representations of the
There were 120 trials in each condition of hue and form,
machine states were esthetically pleasing (eight strongly
divided into 60 control trials and 60 experimental trials. One
agreed and four agreed). Eight felt more compelled to take
trial corresponded to one target-detection task. Only 50% of
care of a machine using the BioAR tree metaphor (two
the trials had a target present (randomly determined). A target
strongly agreed and six agreed), while four thought they
present appeared in only one of the 18 frames of the trial. We
would feel more engaged by a virtual pet (e.g., a cat or a
randomized the trial frame and the position of the target in
dog). Three of the subjects wanted to be able to define their
the matrix. To ensure constant duration, we preloaded the
own metaphor, emphasizing the importance of freedom in
3-D scene for each trial.
defining the relationship between each sensor and its visual
A participant's task was to detect any of the following: 1) a
representation. Two participants mentioned that it would be
red palm tree among green palm trees (hue/control); 2) a red
interesting to include game concepts in the BioAR metamaple tree among green maple trees (hue/control); 3) a red
phor, promoting a competition between the machine's carepalm tree among green trees, half of them palm trees and half
takers/users.
maples (hue/experimental); 4) a red palm tree among red maples
We measured the BioAR prototype's ease of use with the
(form/control); 5) a green palm tree among green maples
System Usability Scale (SUS). The device scored 71.25 points
(form/control); and 6) a red palm tree among maples, half of
(median), which is above the average score of 68 obtained
them red maples and half green maples (form/experimental).
(b)

96 IEEE Consumer Electronics Magazine

^

MAy 2018



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