approaches to personalizing the
user interface must first assess the
user’s functional abilities and subsequently predict effective interface modifications. As an example,
Hurst and colleagues demonstrated
that machine-learning techniques
could be used to distinguish
between mouse movements performed by able-bodied individuals
and individuals whose motor abilities were impaired by age-related
factors or Parkinson’s disease [ 4].
The same methods were also used
to automatically predict whether
individuals would benefit from an
adaptive software technique.
One challenge with assessing
functional ability is that much
past work has relied on observations collected during controlled
lab experiments, which Hurst and
colleagues have demonstrated are
not representative of real-world
performance [ 5]. We believe that it
is important to model text-entry
and pointing abilities based on
observations made unobtrusively
while the users are performing
their own tasks. With accurate and
up-to-the-minute models of what
the user can do, the computers can
guide their users in the selection
of the most promising adaptive
settings or offer to automatically
generate optimal adaptations.
Automatically adapting user
interfaces to a user’s abilities.
Ability assessments can be used
to make appropriate adaptations
to the interface automatically or
in collaboration with the user.
Supple is an example of such a
system (Figure 1) [ 6]. Supple uses
to automatically generate user-optimal interfaces. Supple relies
on a model of how quickly a particular user performs basic user
interface operations to generate
user interfaces predicted to be the
• Figure 1. Ability-based adaptation in SUPPLE: (top) default interface for controlling lighting and
A/V equipment in a classroom; (bottom) interface for the same application automatically generated by SUPPLE for a user with impaired dexterity based on a model of her actual motor abilities.
March + April 2012