Instrumented Usability Analysis for Mobile Devices - Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies - page 3

Figure 1. An example of how sensors and classifiers could be combined to infer user actions during a
usability study, and store these as an annotated log file, allowing developers to correlate different states
with user interaction behaviour. Raw readings are interpreted in a hierarchical fashion by a range of
plug-in classification or signal transformations. These can be arranged hierarchically, so e.g. only if
the person is classified as “walking” do we infer gait phase angle, and whether they are climbing the
stairs or not.

in (Williamson, Murray-Smith, & Hughes, 2007),
which is available for general use by the research
community, from SAMH Engineering Services.
It features a tri-axis accelerometer, tri-axis mag-
netometer, dual channel analogue inputs, dual
channel capacitive sensing and an internal vibrat-
ing motor. Communications are over a Bluetooth
serial port profile. SHAKE includes a powerful
DSP engine, allowing real time linear phase sample
rate conversion. These capabilities allow rapid
prototyping of inertial-sensing-based interfaces
with real-world hardware. But the small size, and
the onboard processor and memory mean that the
device can be used completely separately from the
implementation on a mobile device, and can be
used to log movement at multiple points around
a user in a wide variety of situations. It can be
attached to the back of their device, or could be

attached to their belt, or elsewhere on the body, to
detect activity without wires to restrict a person's
movements.
In this article we suggest that such sensors and
sensors packs can be used in an indirect fashion,
to better understand what was happening to the
device and user at any point in time during a us-
ability experiment. Figure 1 demonstrates how
such a system would work. There could potentially
be multiple sensors placed on the user or mobile
device. Outputs from the sensor would be run
through one or many classifier algorithms that
could infer the user's actions or context at any
one time
Of course, as the algorithms for automatically
inferring context of use from sensors develop to
a level of robustness which allows them to be
used online, they can be used in everyday mobile