Instrumented Usability Analysis for Mobile Devices - Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies - page 17

classification algorithms, during the experiment
to control for experimental stimuli, and adapt the
experimental situation online, providing a more
stringent method of exploring mobile interaction.
The work opens new directions in both design
and usability areas for future work. The specific
results gained through the use of the accelerometer
data for gait analysis allow us to explore new areas
to inform mobile design. For example, one ques-
tion raised from this study is—does designing an
interface such that users tend to tap in preferred
phase ranges lead to quantitatively better perfor-
mance and qualitatively more pleasant user experi-
ence? Might it be better to delay user prompts until
a particular phase region, in order to sustain rhyth-
mic interaction? (See (Lantz & Murray-Smith,
2004) for a discussion of rhythmic interaction).
This suggests experiments deliberately timing
the presentation of prompts, or by using rhythmic
vibrotactile or audio feedback in such a way that
the user is pushed towards tapping in the specific
phase regions. This sensor-conditional feedback
can be generalised, such that specific interventions
can be generated in usability experiments, with a
frequency which is proportional to the probability
of different contexts, allowing users to 'interact
in the wild' while retaining an increased level of
experimental control.
The effects of bias and correlation in tapping
errors can be systematically compensated for in
real time, improving the tapping accuracy. This
information can also be used to automatically adapt
screen layout to walking speed, simplifying and
spreading out the targets as the speed increases.
Further to that, we have the opportunity to
couple the more objective methods of measuring
walking speed used in this article with the exist-
ing literature relating usability to the subjective
use of Percentage Preferred Walking Speeds in,
e.g. (Pirhonen, Brewster, & Holguin, 2002). For
experimental environments that are more difficult
for a user to navigate (such as crowded streets),
these techniques could potentially provide more
information about user disturbances and behav-

iour. The online recognition of context or situations
could be used to have more targeted experiments in
realistic environments, where a particular stimulus
could be presented when the sensors recognise
data compatible with a pre-specified situation.
The experiment described here specifi-
cally examines user performance when walking.
However, the general approach is applicable to
mobile usability studies in general as a method
of gaining more information about the moment
to moment actions of the user. Specifically, it al-
lows us to gain greater insight into user actions
in an uncontrolled environment allowing mobile
usability tests to more easily take place in more
realistic, less laboratory based circumstances. This
work has relevance for tasks such as text entry
or menu navigation in mobile settings. While
this work was tap-based, similar features might
be found in button-pressing, graffiti gestures or
tilt-based interaction.

ACKNOWLEDGMENT

AC & RM-S are grateful for the support of SFI
grant 00/PI.1/C067, and the HEA funded Body
Space project. RM-S, SB & BM are grateful for
the support of EPSRC grant GR/R98105/01, and
IRSCET BRG project SC/2003/271, Continuous
Gestural Interaction with Mobile Devices.

REFERENCES

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