Accessibility. However, further discussion about the degree and scope
of automation requires systematic
exploration of the design space of
adaptive interactions. How should
the user and the system share
the initiative in the adaptation
process? How should the adaptive
mechanisms manage the trade-off
between a user’s familiarity with
the current state of the adapted
user interface and the changes in
her condition that make the current adaptation suboptimal? Is
there a tension between optimizing
the interaction for a user’s abilities
and the possible rehabilitation benefit of working with a slightly more
challenging design? What would
be the psychological effects of the
user interface continuously adapting in a way that makes the user’s
declining abilities apparent?
tools that others can build on.
A critical enabling resource in
research on Personalized Dynamic
Accessibility is data. Whether the
work involves a novel modeling
approach or an automatic adaptation mechanism, abundant data
representative of diverse individuals is key. We can increase availability of this critical resource by
publishing data along with our
papers or by developing methodologies for conducting studies
remotely. Sharing human subjects’
data is challenging because of ethical and regulatory requirements,
but it is possible: Many IRBs will
allow properly anonymized data
to be retained indefinitely and
shared with other researchers.
Such sharing requires foresight,
but once it becomes part of our
research practice, it adds little
extra overhead. Remote experimentation is also a powerful enabler
because sufficient numbers of
participants with a specific set
of abilities may not be available
locally. Such experimentation
is also challenging because the
experimenter must give up some
control over the study environment. Here, recent developments
in the crowdsourcing community
may provide insight into how to
effectively monitor and verify
remote participants’ performance.
participation in today’s society
for people with impairments and
advancing the effectiveness and
quality of mobile interaction.
1. Harada, S., Wobbrock, J.O., and Landay, J.
Voicedraw: A hands-free voice-driven drawing
application for people with motor impairments.
Proc. of the 9th International ACM SIGACCESS
Conference on Computers and Accessibility. ACM,
New York, 2007.
2. Barnard, L., Yi, J.S., Jacko, J.A., and Sears, A.
Capturing the effects of context on human performance in mobile computing systems. Personal
Ubiquitous Comput. 11, 2 (2007), 81–96.
3. Lin, M., Goldman, R., Price, K. J., Sears, A.,
and Jacko, J. How do people tap when walking?
An empirical investigation of nomadic data entry.
International Journal of Human-Computer Studies
65, 9 (2007), 759–769.
4. Hurst, A., Hudson, S.E., Mankoff, J., and Trewin,
S. Automatically detecting pointing performance.
Proc. of the 13th International Conference on
Intelligent User Interfaces. ACM, New York, 2008.
5. Hurst, A., Mankoff, J., and Hudson, S.E.
Understanding pointing problems in real world
computing environments. Proc. of the 10th
International ACM SIGCHI Conf. on Computers and
Accessibility (ASSETS) (Halifax, Nova Scotia). ACM,
New York, 2008, 43-50.
Another area where progress is
urgently needed is in measuring
and modeling users’ abilities. We
need modeling approaches that
can capture the unique abilities of
different individuals from a small
number of observations. We need
models that can be used to answer
questions relevant to design:
6. Gajos, K. Z., Weld, D.S., and Wobbrock, J.O.
Automatically generating personalized user interfaces with Supple. Artificial Intelligence 174, 12-13
(2010), 910–950. doi:10.1016/j.artint.2010.05.005
7. AdaptableGIMP; http://adaptablegimp.org/
8. Kane, S., Wobbrock, J., and Smith, I. Getting off
the treadmill: Evaluating walking user interfaces
for mobile devices in public spaces. Proc. of the
10th International Conference on Human Computer
Interaction with Mobile Devices and Services. ACM,
New York, 2008.
Will the user be able to perform
a particular set of actions with a
particular design? Will she be able
to do so efficiently and with few
errors? Existing models of motor
performance are largely limited to
simple pointing interactions, while
other common desktop operations,
such as scrolling, have not been
studied in as much depth. Going
beyond desktop interactions, no
models currently exist for reason-ing about the accuracy and speed
of modern multitouch gestures
pervasive in mobile computing.
Modeling perceptual and cognitive
abilities is even more challenging. To enable progress, we need
both methods and implemented
ABOUT THE AUTHORS
Krzysztof Z. Gajos is an assistant
professor of computer science at
Harvard University. His research
interests span HCI, AI, and
applied machine learning.
Despite ambitious goals, we
believe we can have substantial real-world impact soon. We
expect that within five years we
will see tools enabling personalized access to Web-based interactive content. Even sooner, we
expect to see mobile applications
that subtly respond to how users’
motor, perceptual, and cognitive
abilities change due to activity and
context. In the long term, we see
Personalized Dynamic Accessibility
as both enabling more equitable
Amy Hurst is an assistant professor of information systems at the
University of Maryland, Baltimore
County. Her research interests
span assistive technology, con-text-aware computing, and interaction design.
Leah Findlater is an assistant pro-
fessor in the College of
Information Studies at the
University of Maryland, College
Park. Her research interests
include personalization, accessi-
bility, and information and com-
munication technologies for development (ICTD).
March + April 2012
© 2012 ACM 1072-5220/12/03 $10.00